european organisation - Eurocontrol

EUROPEAN ORGANISATION FOR THE SAFETY OF AIR NAVIGATION

EUROCONTROL

EUROCONTROL EXPERIMENTAL CENTRE

COSPACE 2003 AIRCRAFT GUIDANCE MODEL BASED EXPERIMENTS EVALUATION OF AIRBORNE SPACING FOR TRAFFIC SEQUENCING EEC Report No. 391 Project AGC-Z-FR

Issued: April 2004

The information contained in this document is the property of the EUROCONTROL Agency and no part should be reproduced in any form without the Agency’s permission. The views expressed herein do not necessarily reflect the official views or policy of the Agency.

REPORT DOCUMENTATION PAGE Reference: EEC Report No. 391

Security Classification: Unclassified

Originator: Cospace Project Sector, Safety and Productivity Business Area, EEC

Sponsor:

Originator (Corporate Author) Name/Location: EUROCONTROL Experimental Centre Centre de Bois des Bordes B.P.15 F – 91222 Brétigny-sur-Orge CEDEX FRANCE Telephone: +33 (0)1 69 88 75 00

Sponsor (Contract Authority) Name/Location:

EUROCONTROL Air-Ground Co-operative Air Traffic Services (AGC) Programme and EUROCONTROL Experimental Centre (EEC) in conjunction with European Commission (EC) Directorate General for Transport and Energy (DG TREN) Trans-European Network for Transport (TEN-T) programme.

EUROCONTROL Agency 96, Rue de la Fusée B-1130 BRUXELLES Telephone: +32 2 729 90 11

TITLE: COSPACE 2003 AIRCRAFT GUIDANCE MODEL BASED EXPERIMENTS EVALUATION OF AIRBORNE SPACING FOR TRAFFIC SEQUENCING Authors Dan Ivanescu (Steria), Chris Shaw, Eric Hoffman, Karim Zeghal

Date

Pages

Figures

Tables

Annexes

References

04/2004

xiv + 55

36

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Project CoSpace AGC-Z-FR

Task No. Sponsor AGC-Z-FR-0000

Period 2002 - 2003

Distribution Statement: (a) Controlled by: EUROCONTROL Project Manager (b) Special Limitations: None (c) Copy to NTIS: YES / NO Descriptors (keywords): Airborne Separation Assistance System – Air Traffic Management – Aircraft Guidance – Airborne Spacing – Fast-Time Experiment – Wind and Turbulence – Mixed Aircraft Type – Traffic Sequencing – Automatic Dependent Surveillance Broadcast.

Abstract: This report presents the results of aircraft guidance model based experiments conducted at the Eurocontrol Experimental Centre (EEC) in 2002 and 2003. These experiments fitted in with a series of human-in-the-loop and model based validation exercises aimed at investigating the use of spacing instructions (denoted airborne spacing) for sequencing of arrival flows. The human-in-the-loop experiments (pilots and controllers) enable an understanding of the impact of airborne spacing on human activity, effectiveness and safety. The model based experiments were intended to complement the validation of airborne spacing by enabling prototyping as well as the collection of a large amount of data under varying conditions. The series of model-based experiments described in this report aimed at: (i) prototyping spacing guidance algorithms for humanin-the-loop experiments; (ii) understanding the intrinsic dynamics of sequences of aircraft under normal and extreme operating conditions (varying entry conditions, wind turbulence and aircraft types); (iii) evaluating the effects of air-air surveillance transmission quality (e.g. ADS-B, Automatic Dependent Surveillance Broadcast, update rate, latency and accuracy) on the performance of airborne spacing. Two complementary operational airborne spacing applications were studied: ‘Merge’ (aircraft on converging trajectories) and ‘Remain’ (aircraft on same trajectory). From the three high level objectives introduced above specific objectives were derived to be studied separately. For the ‘Merge’ application, different distance based guidance laws were developed and compared. The most robust was capable of merging multiple aircraft in descent under turbulent wind conditions. This guidance law was selected for studying the effects of initial distances and speeds on the ability of an aircraft to descend and establish a stable spacing behind another by a given merge waypoint point. For the ‘Remain’ application the effects of ADS-B transmission quality, time based spacing criteria, mixed aircraft types and varying wind conditions on the ability of aircraft to maintain a given along-track spacing (distance or time) behind a descending lead aircraft were investigated. The results presented in the report, based on normal operating conditions, are consistent with the hypothesis that the airborne spacing ‘Merge’ and ‘Remain’ applications are robust. The results based on extreme conditions may be useful to evaluate the limits of applicability for ‘Merge’ and ‘Remain’ applications.

COSPACE 2003 Aircraft Guidance Model Based Experiments

EUROCONTROL

EXECUTIVE SUMMARY This report presents the results of aircraft guidance model based experiments conducted at the Eurocontrol Experimental Centre (EEC) in 2002 and 2003. These experiments fitted in with a series of human-in-the-loop and model based validation exercises aimed at investigating the use of spacing instructions (denoted airborne spacing) for sequencing of arrival flows. The human-in-theloop experiments (pilots and controllers) enable an understanding of the impact of airborne spacing on human activity, effectiveness and safety. The model based experiments were intended to complement the validation of airborne spacing by enabling prototyping as well as collecting a large amount of data under varying conditions. A series of model-based experiments was conducted using MATLAB/Simulink, with essentially an airborne perspective. A previous initial experiment allowed an understanding of the impact of speed and altitude profiles on airborne spacing. The subsequent experiments described in this report aimed at extending the scope as follows. Two complementary operational airborne spacing applications were studied: ‘Merge’ (aircraft on converging trajectories) and ‘Remain’ (aircraft on same trajectory). The three high level objectives below were translated in to specific objectives: (i)

Prototyping spacing guidance algorithms for human-in-the-loop experiments

Four spacing guidance laws for sequencing aircraft on merging trajectories were studied: a linear ‘distance-based’ guidance, a linear ‘speed-based’ guidance, a non-linear guidance based on trajectory prediction and a linear guidance based on the lead groundspeed profile. To evaluate these guidance laws, a set of performance criteria was established along with metrics of spacing distance error and airspeed difference at the merging waypoint and a set of operational scenarios was developed. The ‘distance-based’ linear guidance produced both large spacing distance errors and large speed errors at the merging waypoint. The ‘speed based’ linear guidance achieved near zero error spacing but with very large speed errors for all scenarios. The non-linear guidance produced the desired spacing with minor errors and with the similar aircraft speeds at the waypoint. However, when aircraft are in descent, this guidance becomes difficult to design (complex computations, long simulations requiring high memory resources). The best performing guidance was that based on the lead aircraft groundspeed profile. This guidance was able to merge multiple aircraft in descent under turbulent wind conditions with near-zero error spacing and with the similar aircraft speeds. Therefore the guidance based on lead aircraft groundspeed profile, in the loop with a spacing director and a human pilot model, was selected for further studies. (ii)

Understanding intrinsic dynamics of sequences of aircraft under normal and extreme operating conditions (initial distances and speeds, wind turbulence and aircraft types)

A first experiment was performed to study the effects of entry conditions (initial distances and speeds) on the ability of an aircraft to descend from 29,000 feet to 3,000 feet and establish a stable spacing (8 NM) behind another by a given merge point. Results for two and three aircraft at the same initial speed show how the possible initial spacing error envelope grew, when the initial distance of the lead aircraft to the merge waypoint was increased. The impact on initial spacing error of varying the difference in initial speed was slight. The effects of mixed aircraft types and wind conditions on the ability of an aircraft to maintain a constant time delay along-track spacing behind a descending lead aircraft were then investigated. An exact constant time delay spacing criterion based on lead aircraft position history was used to compare the spacing performance of all combinations of heavy and light aircraft for different wind conditions (with or without turbulence). Results show for both constant and turbulent winds that cross-track winds could have just as detrimental an effect on along-track time based spacing performance as along-track winds.

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Turbulent winds severely degraded time based spacing stability particularly in cross and head wind conditions. Tail winds were the least disturbing for time based spacing. Considering the aircraft type, it was established that a heavy aircraft following a light tended to produce the maximum spacing errors. The other three combinations of aircraft type resulted in similar maximum spacing error behaviour. (iii)

Evaluating effect of air-air surveillance transmission quality (e.g. ADS-B, Automatic Dependent Surveillance Broadcast, update rate, latency and accuracy) on the performance of airborne spacing

The effects of ADS-B transmission quality on the airborne spacing performance of a sequence of six aircraft in descent were investigated. Results show that constant time delay spacing remained more stable than constant distance spacing for degradations in ADS-B update rate, latency and accuracy. Transmission loss compensation terms added to the guidance produced significant improvements in spacing guidance stability and allowed similar performances as in the case without degradations. Conclusions and Future Experiments The results presented in the report are consistent with the hypothesis that the airborne spacing ‘Merge’ and ‘Remain’ applications should be robust under normal conditions. The results based on extreme conditions may be useful to evaluate the limits of applicability for ‘Merge’ and ‘Remain’ applications. Data collected from future human-in-the-loop experiments could be used to improve human pilot and spacing director models. In turn, the subsequent model based simulations should improve the spacing assistance provided to the flight crew for the pilot-in-the-loop simulations. With model based experiments the potential exists to study safety aspects by observing the impact of varying several parameters in a large number of trials (MonteCarlo simulations). Eventual interaction with an ACAS Airborne Collision Avoidance System needs investigation, especially for merge situations.

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ACKNOWLEDGEMENTS

This project is sponsored jointly by the EUROCONTROL Air-Ground Co-operative Air Traffic Services (AGC) programme, EUROCONTROL Experimental Centre (EEC) and European Commission (EC) Directorate General for Transport and Energy (DG TREN) Trans-European Network for Transport (TEN-T) programme.


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TABLE OF CONTENTS LIST OF ANNEXES........................................................................................................... IX LIST OF FIGURES ............................................................................................................ IX LIST OF TABLES............................................................................................................... X REFERENCES .................................................................................................................. XI RELATED PUBLICATIONS ............................................................................................. XII ABBREVIATIONS ........................................................................................................... XIV 1. INTRODUCTION ...........................................................................................................1 2. PRINCIPLES .................................................................................................................2 2.1. 2.2. 2.3.

BACKGROUND.............................................................................................................. 2 CONTEXT OF THE PROJECT ...................................................................................... 2 PREVIOUS WORK......................................................................................................... 3

3. OBJECTIVES AND DEFINITIONS ...............................................................................4 3.1. 3.2.

OBJECTIVES ................................................................................................................. 4 DEFINITIONS................................................................................................................. 5 3.2.1. ‘Merge’ Application ............................................................................................5 3.2.2. ‘Remain’ Application ..........................................................................................7 3.2.3. ADS-B Transmission Quality .............................................................................8

4. GUIDANCE DESIGN.....................................................................................................9 4.1.

4.2.

‘MERGE’ APPLICATION ................................................................................................ 9 4.1.1. Quality Criteria...................................................................................................9 4.1.2. Linear Guidance ................................................................................................9 4.1.3. Non-Linear (‘Bang-bang’) Guidance................................................................10 4.1.4. Lead Groundspeed Based Guidance ..............................................................12 ‘REMAIN’ APPLICATION ............................................................................................. 13 4.2.1. Quality Criteria.................................................................................................13 4.2.2. Linear Guidance ..............................................................................................13

5. APPARATUS ..............................................................................................................15 5.1. 5.2. 5.3.

5.4.

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FAST-TIME SIMULATION ENVIRONMENT ................................................................ 15 ADS-B MODEL............................................................................................................. 15 AIRCRAFT MODEL...................................................................................................... 16 5.3.1. Aircraft Dynamics Model..................................................................................16 5.3.2. Autopilot Model................................................................................................17 5.3.3. Spacing Director ..............................................................................................18 5.3.4. Human Pilot Model ..........................................................................................18 ATMOSPHERE AND WIND MODELS ......................................................................... 18



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6.

METHOD ..........................................................................................................21

6.1.

‘MERGE’ EXPERIMENTS ............................................................................................ 21 6.1.1. Comparison of Linear and Non-Linear Guidance ............................................21 6.1.2. Evaluation of Lead Groundspeed Based Guidance and Spacing Director......22 6.1.3. Effect of Entry Conditions ................................................................................22 ‘REMAIN’ EXPERIMENTS ........................................................................................... 24 6.2.1. Effect of ADS-B Transmission Quality.............................................................24 6.2.2. Evaluation of Time-Based Spacing Criteria.....................................................25 6.2.3. Effect of Mixed Aircraft Types and Varying Wind Conditions ..........................26

6.2.

7. RESULTS....................................................................................................................27 7.1.

7.2.

‘MERGE’ EXPERIMENTS ............................................................................................ 27 7.1.1. Comparison of Linear and Non-Linear Guidance ............................................27 7.1.2. Evaluation of Lead Groundspeed Based Guidance and Spacing Director......28 7.1.3. Effect of Entry Conditions ................................................................................29 ‘REMAIN’ EXPERIMENTS ........................................................................................... 32 7.2.1. Effect of ADS-B Transmission Quality.............................................................32 7.2.2. Evaluation of Time-Based Spacing Criteria.....................................................36 7.2.3. Effect of Mixed Aircraft Types and Varying Wind Conditions ..........................37

8. SUMMARY OF MAIN RESULTS ................................................................................41 8.1. 8.2.

SUMMARY OF ‘MERGE’ RESULTS............................................................................ 41 SUMMARY OF ‘REMAIN’ RESULTS ........................................................................... 42

9. CONCLUSIONS ..........................................................................................................44 9.1.

FUTURE EXPERIMENTS ............................................................................................ 44

FRENCH TRANSLATION (TRADUCTION EN LANGUE FRANÇAISE) ..........................45 LIST OF ANNEXES Annex

..................................................................................................................................... 51

LIST OF FIGURES Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11: Figure 12:

‘Merge’, cockpit display of traffic information.................................................................. 6 ‘Merge’ example ............................................................................................................. 6 ‘Remain’, cockpit display of traffic information................................................................ 7 ‘Remain’ example ........................................................................................................... 7 A speed-time graph ...................................................................................................... 11 Speed-time graph of bang-bang control: demand and response ................................. 11 Closed loop control law system block diagram with human pilot model....................... 18 The wind model components ....................................................................................... 19 Turbulent wind versus altitude - JAR-AWO model (20 knots wind at 30 feet AGL)...... 19 ‘Merge’ example – three aircraft ................................................................................... 22 ‘Merge’ example – admissible entries of trail................................................................ 23 Comparison of absolute distance spacing error at the merge waypoint ....................... 27


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Figure 13: Comparison of absolute magnitude of groundspeed difference at the merge waypoint ............................................................................................................ 27 Figure 14: a) Spacing distance error between aircraft b) Comparison of true airspeed and groundspeed of the lead and trailing aircraft....... 28 Figure 15: Comparison between the desired CAS given by the guidance law and the demanded CAS from the spacing director (as input to the pilot model) ....................... 28 Figure 16: Entry conditions – initial spacing error bounds for a sequence of two aircraft having the same initial speeds ..................................................................................... 29 Figure 17: Entry conditions – initial spacing error for a sequence of two aircraft having different initial speeds................................................................................................... 30 Figure 18: a) Entry conditions–initial spacing error for a sequence of three aircraft with the same speeds b) Initial position for a sequence of three aircraft with the same speeds ........ 31 Figure 19: Impact of ADS-B update rate on in-trail performances: maximum spacing error (%) as function of update period. Solid lines: basic guidance law, dashed lines: guidance with update rate compensation. .................................................................... 32 Figure 20: Engine throttle oscillations (%) function of probability of reception of ADS-B, guidance with update rate compensation ..................................................................... 33 Figure 21: Impact of ADS-B latency on spacing error, basic guidance law ................................... 33 Figure 22: Impact of ADS-B latency on spacing error, guidance with latency compensation ........ 34 Figure 23: Impact of ADS-B accuracy on in-trail spacing performance. ........................................ 35 Figure 24: Impact of ADS-B accuracy on in-trail performance....................................................... 35 Figure 25: ACTD - true airspeed behaviour ................................................................................... 36 Figure 26: CTD - true airspeed behaviour ..................................................................................... 36 Figure 27: CTD and ACTD, time spacing error comparison for a continuous update rate............. 37 Figure 28: Turbulent cross wind effects on largest of maximum spacing errors ........................... 39 Figure 29: Turbulent head wind effects on largest of maximum spacing errors............................. 39 Figure 30: Turbulent tail wind effects on largest of maximum spacing errors ................................ 39 Figure 31: Turbulent cross wind effects on mean of maximum spacing errors.............................. 39 Figure 32: Turbulent head wind effects on mean of maximum spacing errors .............................. 39 Figure 33: Turbulent tail wind effects on mean of maximum spacing errors.................................. 39 Figure 34: a) Time spacing errors for constant head and tail winds b) Equivalent distance based spacing errors for constant head and tail winds ............ 40 Figure A-1: Simulation results check-case...................................................................................... 53 Figure A-2: Simulation results check-case...................................................................................... 54

LIST OF TABLES Table 1: Table 2: Table 3: Table 4: Table 5: Table 6: Table 7: Table 8: Table 9:

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Application chosen to study for each specific objective: ‘Yes’ means a performed study, ‘No’ means a potential future study...................................................................... 5 Navigation Accuracy Categories (NAC)- Position and Velocity (RTCA, 2002)............. 16 Mean wind speed increasing with altitude and function of wind measured at 30 feet AGL .................................................................................................................. 20 Initial conditions for comparing scenarios..................................................................... 21 The wind parameters.................................................................................................... 24 ADS-B experimental parameters and perturbation ranges........................................... 25 Mixed aircraft types and winds: experimental parameters ........................................... 26 Current status of the fast-time simulations: ‘Yes’ in the table means a performed study, ‘No’ means a potential future study................................................................... 41 ‘Remain’: performance of improved spacing guidance for realistic ADS-B transmission ................................................................................................................. 42



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REFERENCES [1]

Abbott, T.S., (2002): “Speed Control Law for Precision Terminal Area In-Trail Self Spacing”, NASA Technical Memorandum 211742, NASA Langley Research Center, Virginia, USA.

[2]

Federal Aviation Administration/EUROCONTROL, (2001): “Principles of Operation for the Use of Airborne Separation Assurance Systems”, FAA/EUROCONTROL Cooperative R&D.

[3]

Fokker, (1989): “Flight Simulator Data for the Fokker F28 mk100”, Report L-28-468, issue 2, September, Restricted.

[4]

Grimaud, I., Hoffman, E., Rognin, L. and Zeghal, K., (2003): “EACAC 2001 Real-Time Experiment”, EUROCONTROL Experimental Centre (EEC) Report No. 380 and 380-2, Vol.1 and 2, Released Issue: March 2003, EUROCONTROL Experimental Centre, France.

[5]

Hammer, J., (2000): “Case Study of Paired Approach Procedure to Closely Spaced Parallel Runways”, Air Traffic Control Quarterly, Vol. 8(3), pp. 223–252.

[6]

Hanke, J. and Nordwall, S., (1995): “The Simulation of a Jumbo Jet Transport aircraft”, NASA Report D6-30643, NASA Langley Research Center, Virginia, USA.

[7]

Hoffman, E., (1993): “Contribution to Aircraft Performance Modelling for ATC use”, EEC Report 258, EUROCONTROL Experimental Centre, France.

[8]

Joint Aviation Authorities, (1996): “Joint Aviation Requirement-All Weather Operations”, JAR 07/03-13, Netherlands.

[9]

Kelly, J.R., (1983): “Effect of Lead-Aircraft Ground-Speed Quantization on Self Spacing Performance Using a Cockpit Display on Traffic”, NASA Technical Paper 2194, NASA Langley Research Center, Virginia, USA.

[10] Lambregts, A.A., (1983): “Integrated System Design for Flight and Propulsion Control Using Total Energy Principles”, AIAA Aircraft Design, Systems and Technology Neeting, AIAA Paper 83-2561, Texas, USA. [11] Mathworks Inc., (2003): “Matlab Users Guide”, Mathworks, Natick, Massachusetts, USA. [12] Mathworks Inc., (2003): “Simulink Users Guide”, Mathworks, Natick, Massachusetts, USA. [13] McGruer, D., (1973): “Aircraft Dynamics and Automatic Control”, Princeton University Press. [14] NLR National Aerospace Laboratory, (2002): “AMAAI Modelling Toolset for the Analysis of In-trail Following Dynamics”, NLR Report CR-2002-044, Netherlands. [15] Pritchett, A.R. and Yankosky L.J., (1998): “Simultaneous Design of Cockpit Display of Traffic Information & Air Traffic Management Procedures”, SAE Transactions - Journal of Aerospace, Paper 985543. [16] Pritchett, A.R. and Yankosky L.J., (2000): “Pilot Performance at New ATM Operations: Maintaining In-Trail Separation and Arrival Sequencing”, Proceedings of AIAA Guidance, Navigation, and Control Conference, Colorado, USA.


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[17] RTCA, (1998): “Minimum Aviation System Performance Standards for Automatic Dependent Surveillance Broadcast (ADS-B)”, RTCA Paper DO-242 / SC186, RTCA Inc., USA. [18] RTCA, (2000): “Operational Concepts for Cockpit Display of Traffic Information (CDTI) Initial Applications”, RTCA Paper DO-259 / SC186, RTCA Inc., USA. [19] RTCA, (2002): “ Minimum Aviation System Performance Standards for Automatic Dependent Surveillance Broadcast (ADS-B)”, RTCA Paper DO-242A / SC186, RTCA Inc., USA. [20] Sorensen, J.A. and Goka, T., (1983): “Analysis of In-Trail Following Dynamics of CDTIEquipped Aircraft”, Journal of Guidance, Control and Dynamics, Vol. 6, pp 162-169. [21] Vinken, P., Hoffman, E., Zeghal, K., (2000): “Influence of Speed and Altitude Profile on the Dynamics of In-trail Following Aircraft”, Proceedings of AIAA Guidance, Navigation and Control Conference, Colorado, USA. [22] Zeitlin, A.D., (2001): “Safety Assessments of ADS-B and ASAS”, 4th USA / Europe Air Traffic Management R&D Seminar, New Mexico, USA.

RELATED PUBLICATIONS •

Ivanescu, D., Hoffman E. and Zeghal K., (2002a): “Impact of ADS-B Link Characteristics on the Performances of In-Trail Following Aircraft”, Proceedings of AIAA (American Institute of Aeronautics and Astronautics), Guidance, Navigation, and Control Conference, Monterrey, California, USA.

•

Ivanescu, D., Hoffman, E., Shaw C. and Zeghal K., (2002b): “Analysis of Spacing Guidance for Sequencing Aircraft on Merging Trajectories”, 21st Digital Avionics Systems Conference, Irvine, California, USA.

•

Hoffman, E., Ivanescu, D., Shaw, C. and K. Zeghal, (2003a): “Analysis of Constant Time Delay Airborne Spacing Between Aircraft of Mixed Types in Varying Wind Conditions”; Proceedings of 5th USA / Europe Air Traffic Management R&D Seminar, Budapest, Hungary.

•

Hoffman, E., Ivanescu, D., Shaw, C. and K. Zeghal, (2003b): “Effect of Mixed Aircraft Types and Wind on Time Based Airborne Spacing”, Proceedings of AIAA Guidance, Navigation, and Control Conference, Austin, Texas, USA.

•

Ivanescu, D., Shaw, C., Hoffman, E. and K. Zeghal, (2003): “Effect of Entry Conditions on Airborne Spacing when Sequencing Multiple Converging Aircraft”, Proceedings of AIAA Guidance, Navigation, and Control Conference, Austin, Texas, USA.

•

Hoffman, E., Ivanescu, D., Shaw, C. and Zeghal K., (2003): “Effect of Automatic Dependent Surveillance Broadcast (ADS-B) Transmission Quality on the Ability of Aircraft to Maintain Spacing in a Sequence”, Air Traffic Control Quarterly Special Issue: Aircraft Surveillance Applications of ADS-B, Vol. 11(3).

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CoSpace Project Reports: Aligne, F., Grimaud, I., Hoffman, E., Rognin, L. and K. Zeghal, (December 2003): “CoSpace 2002 controller experiment assessing the impact of spacing instructions in E-TMA and TMA Volume I and II”, EEC Report No. 386, Eurocontrol Experimental Centre. Hebraud, C., Hoffman, E., Papin, A., Pene, N., Rognin, L., Sheehan, C. and K. Zeghal, (February 2004): “CoSpace 2002 flight deck experiments assessing the impact of spacing instructions from cruise to initial approach Volume I and II”, EEC Report No. 388, Eurocontrol Experimental Centre.

CoSpace Project Web Site: www.eurocontrol.fr/projects/cospace/


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ABBREVIATIONS Abbreviation ACAS

Airborne Collision Avoidance System

ACTD

Approximate Constant Time Delay

ADS-B

Automatic Dependant Surveillance – Broadcast

AGL ASAS

Above Ground Level Airborne Separation Assistance System

ATC

Air Traffic Control

ATM

Air Traffic Management

CAS

Calibrated Air Speed

CD

Constant Distance

CTD

Constant Time Delay

CDTI

Cockpit Display of Traffic Information

EEC

EUROCONTROL Experimental Centre

FAA

Federal Aviation Administration

FL

Flight Level

GS

Ground Speed

ISA

International Standard Atmosphere

JAR

Joint Aviation Authorities

NAC

Navigation Accuracy Categories

NACp

NAC horizontal position

NACv

NAC velocity

NM

Nautical Mile

PI RTCA TAS

xiv

De-Code

Proportional Integral Radio Technical Commission for Aeronautics True Air Speed

TIS-B

Traffic Information Service – Broadcast

TMA

TerMinal control Area



1.

EUROCONTROL

INTRODUCTION

This report presents the results of aircraft spacing guidance model based experiments conducted at the Eurocontrol Experimental Centre (EEC) in 2002 and 2003. These experiments fitted in with a series of human-in-the-loop and model based validation exercises aimed at investigating the use of spacing instructions (denoted airborne spacing) for sequencing of arrival flows. The human-in-theloop experiments (pilots and controllers) enable an understanding of the impact of airborne spacing on human activity, effectiveness and safety. The model based experiments were intended to complement the validation of airborne spacing by enabling prototyping as well as collecting a large amount of data under varying conditions. A series of model-based experiments is conducted using MATLAB/Simulink, with essentially an air perspective. A previous initial experiment allowed an understanding of the impact of speed and altitude profiles on airborne spacing. The subsequent experiments described in this report aimed at extending the scope of the initial experiment by: (i)

prototyping spacing guidance algorithms for the human-in-the-loop experiments;

(ii)

understanding intrinsic dynamics of sequences of aircraft under normal and extreme operating conditions (varying entry conditions, wind turbulence and aircraft types);

(iii)

evaluating the air-air surveillance transmission quality (ADS-B update rate, latency and accuracy) on the performances of airborne spacing.

The document is organised as follows: •

Section 2 introduces the principles, the background and the project context.

•

Section 3 presents the objectives and briefly defines the operational context: airborne spacing applications ‘Merge’ and ‘Remain’ and ADS-B transmission quality.

•

Section 4 describes the corresponding guidance laws.

•

Section 5 describes the model based environment: aircraft, ADS-B, wind and atmosphere models, MATLAB/Simulink platform.

•

Section 6 gives the experiment set-up.

•

Section 7 presents the main findings with graphs and discussions.

•

Section 8 summarises main results and makes recommendations.

•

Section 9 presents the conclusions.

•

French translation of executive summary, introduction, objectives, recommendations and conclusions.

•

Appendix of aircraft model validation.

A series of documented experiments with corresponding prototype guidance systems was produced addressing each of the above goals. Results of experiments were published at conferences where appropriate (see ‘Related Publications’ section).


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2.


PRINCIPLES

2.1. BACKGROUND Air Traffic Management (ATM) concepts are currently being evaluated where air traffic controllers may have the possibility to give some spacing related instructions to the flight deck. For example, in zones of convergence, pilots could be instructed to establish and maintain a prescribed spacing with respect to an aircraft in front (lead) by monitoring the spacing and adjusting their own speed [FAA/EUROCONTROL, 2001]. Possible operational benefits could be fewer time critical instructions, more strategic ways of working and greater traffic awareness for pilots (see CoSpace real time experiments [Grimaud et al. 2003], and references therein). It is expected that the increased controller availability could lead to improved safety, which in turn could enable better efficiency and/or, depending on airspace constraint, more capacity. In addition, it is expected that flight crew could gain in awareness and anticipation by taking an active part in the management of the situation with respect to the concerned aircraft. In this context the air-to-air surveillance technology ADS-B is one of the potential key enablers required to support the implementation of such an application. Surveillance information, such as position and velocity of the lead aircraft, is transmitted from the lead aircraft to the trailing aircraft by ADS-B (or TIS-B) [RTCA, 1998, 2000, 2002] and may be displayed to the pilot on a Cockpit Display of Traffic Information (CDTI) as part of an Airborne Separation Assistance System (ASAS). 2.2. CONTEXT OF THE PROJECT The objective of CoSpace is to assess the operational feasibility, benefits and limits of the use of spacing instructions (“airborne spacing”) as well as to identify the evolutions induced or required with respect to today’s ATC (Air Traffic Control). CoSpace covers concept definition up to validation aspects including human-in-the-loop and model-based simulations. The focus is on the sequencing of arrival flows from cruise to initial approach (extended TMA – TerMinal control Area ) and more recently down to final approach (TMA). Since its inception in 1998, the project follows an iterative process, in which every step helps defining the next one. Because of the inherent air-ground nature of the concept, both controller and pilot aspects have to be considered. After an initial air-ground human-in-the-loop simulation, two series of air and ground simulations were conducted: five ground simulations with in total 34 controllers from different European countries over 14 weeks; four air simulations with in total 31 test and airline pilots over 25 days. The consistency between the two series is ensured essentially by relying on the same concept (applications, procedures and phraseology), the same operational environment (type of airspace, scenarios) and a unified validation framework (experimental plan, metrics). Model-based simulations have been introduced to complement the validation of airborne spacing. A first series of aircraft guidance model-based simulations has been conducted, with essentially an air perspective. They rely on the modelling of aircraft, autopilot, spacing guidance and pilot behaviour. This series of simulations allows some understanding of the intrinsic dynamics of sequences of aircraft, the impact of airborne surveillance characteristics (e.g. ADS-B update rate), the effect of wind and of aircraft type. The data collected during the pilot-in-the-loop simulations allowed refinement of the pilot model. In turn, the subsequent model-based simulations allow improvements of the spacing assistance provided to the flight crew for the pilot-in-the-loop simulations, and to refine the experimental plan (e.g. selection of scenarios).

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A second series of model-based simulations was introduced which a controller perspective. The modelling investigated the impact of airborne spacing on controller workload in the extended TMA. The controller was modelled by defining standardised ATC tasks performed with and without airborne spacing. The allocation of these tasks during the simulations enabled the impact of spacing instructions on controller workload to be assessed. This modelling sought to provide objective workload results to extend the subjective results collected during the November 2002 Real Time Controller Simulation. 2.3. PREVIOUS WORK Sequencing aircraft applications were widely studied in the literature, first solutions being obtained in the 1980’s [Sorensen and Goka, 1983]. The study covered both analytical and experimental aspects. Three separation criteria were introduced (constant distance, constant time predictor, and constant time delay). To simulate strings of aircraft a mathematical model was used. In a further analysis ([Kelly, 1983] and the references therein) two separation criteria were investigated through pilot-in-the-loop experiments with spacing cues on the cockpit display. In the 1990’s, computer based models were used along with pilot-in-the-loop [Pritchett and Yankosky, 1998, 2000]. In recent years the impact of top of descent [Vinken et al. 2000] and speed reduction on in-trail following (‘Remain’) aircraft performance was investigated through mathematical models, and more advanced guidance algorithms were proposed [Abott, 2002]. Merging applications seemed to be less well studied. [Grimaud et al, 2000] developed a detailed operational concept for a ‘Merge’ application. Real-time simulations show that it is important that both air traffic controller and pilots have a clear understanding of whether the complete ‘Merge’ manoeuvre is feasible at the time the instruction is issued. In [Hoffman et al, 2002] three different guidance controllers for merging pairs of aircraft at a fixed waypoint in level flight with no wind were investigated. Although standards are under development and initial recommendations are proposed [RTCA, 2002], [Zeitlin, 2001], the impact of ADS-B characteristics on the performances of the considered applications remains a key issue. An experimental study focussing on closely spaced parallel approaches has been carried out by [Hammer, 2000]. Concerning the realism of the past studies on sequencing aircraft guidance, most of them reviewed tended to assume perfect ADS-B transmission quality, similar aircraft performance [Pritchett and Yankosky 2000] and simple constant wind models. To add to the current body of knowledge on airborne spacing for sequencing and merging, our studies therefore addressed the following aspects: (i) ‘Merge’ and ‘Remain’ applications (ii) distance and time based spacing guidance (iii) mixed aircraft types (iv) different constant and turbulent wind conditions (v) different ADS-B update rates, latencies and accuracies (vi) different entry conditions.


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3.

OBJECTIVES AND DEFINITIONS

3.1. OBJECTIVES For each application, the three high level objectives introduced above were translated in to specific objectives as follows:

High level objectives

Specific objectives

• Prototyping of spacing guidance algorithms for the human-in-the-loop experiments

• •

•

4

Increase understanding of intrinsic dynamics of sequences of aircraft under normal and extreme operating conditions

•

Evaluate effect of air-air surveillance transmission quality on the performance of airborne spacing

•

•

Develop guidance law (‘Remain’ and ‘Merge’). Develop spacing director system (‘Merge’). Develop time based spacing criteria (‘Remain’).

Measure effect on spacing performance of initial position and speed (‘Merge’). Measure effect on spacing performance of heavy and light aircraft types (‘Remain’). Measure effect on spacing performance of constant and turbulent wind conditions (‘Remain’).

Measure effect on spacing performance of ADS-B update rate, latency and accuracy (‘Remain’).



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Representative cases were studied as follows: Table 1: Application chosen to study for each specific objective: ‘Yes’ means a performed study, ‘No’ means a potential future study. Measure effect on spacing performance of initial position and speed

Measure effect on spacing performance of heavy and light aircraft

Measure effect on spacing performance of constant and turbulent wind conditions

Measure effect on spacing performance of ADS-B transmission quality

Application

Develop guidance laws

Develop spacing director

Develop time based spacing criteria

‘Merge’

Yes

Yes

No

Yes

No

No

No

‘Remain’

Yes

No

Yes

No

Yes

Yes

Yes

3.2. DEFINITIONS 3.2.1. ‘Merge’ Application The ‘Merge’ application involves an air traffic controller asking a pilot to select1 a neighbouring aircraft as a target on a CDT I (see Figure 1). An example of the phraseology developed is: Controller: “DLH456, select target 1234” Pilot of DLH456: “Selecting target 1234, DLH456” Once the target has been selected, the air traffic controller can then ask the pilot to ‘Merge’ behind the target at a given waypoint ahead with a given spacing. An example of the phraseology developed is: Controller: “DLH456, behind target, merge waypoint 8 NM behind” Pilot of DLH456: “Merging way point, 8 NM behind target, DLH456” where the spacing could be defined as a distance in nautical miles (NM) or time.

1 Note: The target is identified by a unique code other than the callsign to avoid confusion over the radio communication ‘party-line’.


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Figure 1: ‘Merge’, cockpit display of traffic information

An example of the ‘Merge’ application is illustrated in Figure 2. The two aircraft, the lead (target) and the trail aircraft, are flying direct to the same fixed waypoint. The solid arrows represent the current position and track angle of the aircraft, and the dashed arrows represent the desired positions of the two aircraft when the lead will reach the waypoint. At the moment the lead reaches the waypoint the spacing between aircraft must be equal to the desired spacing within a defined tolerance, and the aircraft should have similar speeds. After the waypoint the problem is similar to the in-trail following aircraft situation, i.e. each aircraft follows its own trajectory within a sequence maintaining the spacing between itself and the aircraft immediately in front. Mathematically, this concept can be expressed as follows: let dtrail and dlead be the distances from the current positions of the respective aircraft to the waypoint. When the lead reaches the waypoint (dlead=0), the spacing error (derror) is the difference between the remaining distance of the trailing aircraft to the waypoint and the desired spacing: d error = d trail

( d lead ∗ = 0 )

(3.1)

− d spacing

‘Merge’ “ Behind target, merge WPT 8NM behind”

WPT

AFR123 235 ↓ 40

D

DLH456 250 ↓ 41

Figure 2: ‘Merge’ example

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3.2.2. ‘Remain’ Application “Remain” is the second airborne spacing application studied (Figure 3 and Figure 4). The ‘Remain’ application involves an air traffic controller asking a pilot to select a neighbouring aircraft as a target on a CDTI as in ‘Merge’. An example of the phraseology developed is: Controller: “DLH456, select target 1234” Pilot of DLH456: “Selecting target 1234, DLH456” Once the target has been selected, the air traffic controller can then ask the pilot to maintain a given longitudinal distance or time behind the target. An example of the phraseology developed is: Controller: “DLH456, behind target, remain 8 NM behind” Pilot of DLH456: “Remaining 8 NM behind target, DLH456” where the spacing could be defined as a distance in nautical miles (NM) or time.

Figure 3: ‘Remain’, cockpit display of traffic information

‘Remain’ “Behind target, remain 8NM behind”

AFR123 235 ↓ 40

D DLH456 250 ↓ 41

Figure 4: ‘Remain’ example


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3.2.3. ADS-B Transmission Quality Concerning the ADS-B transmission quality, the following three characteristics were considered:

8

•

Update rate of reports: sustained rate at which periodic ADS-B reports were received.

•

Latency of transmission: delay between the time when the ADS-B report was received and processed, and the time when position and velocity were measured. This included not only ADS-B latency, but also additional delays in the processing of the information.

•

Accuracy of surveillance information: difference between the state vector transmitted by ADS-B and the true values.



4.

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GUIDANCE DESIGN

4.1. ‘MERGE’ APPLICATION A first theoretical study was devoted to finding appropriate guidance for establishing a desired spacing and speed between aircraft when merging in flight. Various guidance spacing criteria and guidance laws were designed and tested during the period October 2002 – March 2003. The main results are presented in the following sub-section. The most appropriate guidance design was chosen for further studies. 4.1.1. Quality Criteria Such guidance laws aim at establishing a given spacing at the waypoint to a lead aircraft through speed adjustments as a pilot or as cockpit automation might do. The guidance law receives surveillance data from the lead aircraft and feeds the desired target calibrated airspeed input ( CAS traildesired ) of the aircraft model. The target altitude (hdes) is fed independently, and depends on the top of descent scenario. The following criteria were considered when designing ‘merge’ spacing guidance laws: i)

Spacing error at the merging waypoint should be small.

ii)

The guidance should be robust against large initial spacing errors.

iii)

The trailing aircraft should have a similar speed to that of the lead aircraft for a smooth transition in to a ‘Remain’ application after the waypoint.

iv)

The frequency of speed changes asked of the pilot in the trailing aircraft should be low enough to be operationally acceptable.

v)

The speed profile of the trailing aircraft should be smooth with minimal speed deviations because it may itself be a target leading another trailing aircraft behind.

4.1.2. Linear Guidance First linear guidance laws were developed starting from initial ideal conditions (i.e. no spacing error, or near zero). In a classical linear approach, common pre-defined spacing criteria can be employed to predict and characterise the desired spacing at the waypoint. A guidance law was then designed to control and minimise the spacing error. Based on such prediction, this spacing error was computed in two ways: 4.1.2.1. Linear Guidance: ‘Distance-Based’ Prediction The simplest solution seemed to be adapting the already existing criteria defined in [Pritchett and Yankosky, 1998], [Sorensen and Goka, 1983] for the ‘in-trail’ concept. The use of the same criteria for both concepts (i.e. ‘merging’ and ‘in-trail’) facilitates the passage from one operational mode (‘merging’) to the other (‘in-trail’).


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If no prediction based on current speeds is considered, equation (3.1) can be approximated as the difference between the two aircrafts’ current distance to the waypoint taking into account the desired spacing:

d error = d trail − d lead − d spacing

(4.1)

4.1.2.2. Linear Guidance: ‘Speed-Based’ Prediction The ‘distance-based’ prediction criteria can be enhanced using the current aircraft speeds (Vtrail, Vlead). Assuming that these speeds are constant, the predicted spacing at the waypoint can be approximated as follows: The predicted time needed for the lead aircraft to reach the waypoint is: t

leadtogo

=

d V

lead lead

(4.2)

In the meantime, the trailing aircraft covers a distance equal to: dist = t

leadtogo

⋅V

trail

=d

lead

V ⋅ trail V lead

(4.3)

Finally the spacing error is defined as: V derror = dtrail − (dlead ⋅ trail ) − d spacing Vlead

(4.4)

4.1.2.3. Linear Guidance Law The spacing error ‘ d error ’ was controlled by a corresponding linear guidance law. In both cases this guidance law had the following form:

(

) (K s+ s )

Vcmd = K p ⋅ d error ⋅ s 2 + 2ζ manω man ⋅ s + ω man ⋅ 2

i

(4.5)

Details about the guidance can be found in [NLR, 2002], [Hoffman et al. 2003]. 4.1.3. Non-Linear (‘Bang-bang’) Guidance Time and speed are inherently related therefore to meet simultaneous time and speed constraints a better performing guidance law could take the coupling into account. •

Speed-time graph analysis.

The problem of meeting a simultaneous time and speed constraint at a waypoint can be more generally analysed using a speed-time graph (Figure 5), where speed is the groundspeed.

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The start point, end point and area under the graph are completely defined by the constraints: initial speed, initial time, final speed, final time and distance to go to waypoint (area under graph). final_speed

distance_to_go =

∫ speed(t)dt

(4.6)

initial_speed

The problem is reduced to finding the shape of the speed profile within the bounds of operational feasibility.

Figure 5: A speed-time graph

Maximum speed, minimum speed, acceleration and deceleration must be within the normal flight operating envelope and use of speed targets should be consistent with normal speed guidance e.g. constant CAS descent. A ‘bang-bang’ controller was designed to provide the following speed profile characteristics (see Figure 6):

Figure 6: Speed-time graph of bang-bang control: demand and response

Depending on whether the trailing aircraft is late/early, an expedite acceleration/deceleration to a constant maximum/minimum speed was demanded for a certain duration followed by an expedite deceleration/acceleration to the final speed. The duration of the intermediate speed target was calculated from the given constraints using the formula: step_durat ion =

D0 − final_spee d ⋅ final_time speed_limit − final_speed


(4.7)

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Where Do is distance to go to the waypoint and speed_limit can be a maximum or minimum speed depending on whether the trailing aircraft is late or early. The formula is based on the area of the two rectangles under the target speed profile as an approximation to the actual distance (area under actual speed profile). The area calculation is simple and the errors in lag between acceleration and deceleration tend to cancel. Maximum and minimum speeds were chosen for expedite manoeuvres and assumed fixed for the duration of the capture. 4.1.4. Lead Groundspeed Based Guidance A third spacing guidance law was derived using the criteria in section 4.1.1: CAS traildesired = GS → CAS conversion (GS traildesired )

(4.8)

where the desired CAS of the trailing aircraft CAS traildesired is derived by converting the desired groundspeed of the trailing aircraft GS traildesired to the equivalent CAS. The desired groundspeed of the trailing aircraft is based on the current groundspeed of the lead aircraft GS lead plus a corrective speed term derived from the spacing distance error d error divided by the time to go before the lead aircraft arrives close to the merging waypoint t leadtogo . d GS traildesired = GS lead + t error leadtogo

(4.9)

The spacing distance error is defined as the difference between the distance of the trailing aircraft to the merge waypoint d trail , the distance of the lead aircraft to the merge waypoint d lead and the desired spacing distance d spacing : d error = d trail − d lead − d spacing

(4.10)

t leadtogo is given by:

If d lead ≥ 2d spacing then t leadtogo =

dlead −d spacing GSlead

(4.11)

else t leadtogo =

12

d spacing GSlead

(4.12)



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4.2. ‘REMAIN’ APPLICATION 4.2.1. Quality Criteria The following criteria were considered when designing ‘remain’ spacing guidance law: i)

Spacing error between two consecutive aircraft should be small.

ii)

The guidance should be robust against initial spacing errors or speed changes.

iii)

All responses should exhibit well-damped dynamic behaviour.

iv)

The speed profile of the trailing aircraft should be smooth with minimal speed deviations because it may itself be a target lead for another trailing aircraft behind.

4.2.2. Linear Guidance 4.2.2.1. Spacing Criteria Again, pre-defined along-track spacing criteria were employed to characterize the desired spacing. To distinguish them from the ‘Merge’ criteria ‘ylead’ and ‘ytrail’ were used here as notation for alongtrack distances. Constant Distance (CD) Spacing Criterion: This criterion was based on the spacing distance between two aircraft. The spacing error (yerrorCD) was calculated from the difference between the lead aircraft’s position (ylead) and the trailing aircraft position (ytrail) taking into account the desired spacing (yspacing): y errorCD = y lead − y trail − y spacing

(4.13)

Where positions were measured by along track distance from a common origin. Constant Time Delay Criterion (CTD): The CTD criterion was based on constant time spacing. Spacing error was defined as the difference between the elapsed time since the lead aircraft overflew the current trailing aircraft position, and the desired time spacing:

t errorCTD = t − t * | ylead ( t * ) = ytrail ( t ) − t spacing

(4.14)

The CTD criterion was rewritten as an equivalent constant distance-based criterion:

yerrorCTD (t ) = ylead (t − t spacing ) − ytrail (t )

(4.15)

The re-formulation of the CTD spacing criterion as a constant distance criterion was used in the design and implementation of the guidance law controller. Approximate Constant Time Delay (ACTD) Spacing Criterion: The ACTD spacing criterion was based on the CTD spacing criterion, which, assuming the lead aircraft speed (VleadTAS) was constant, was approximated as:

terrorACTD =

ylead − ytrail − t spacing VleadTAS


(4.16) 13


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The ACTD spacing criterion was rewritten as an equivalent constant distance-based criterion: y errorACTD = y lead − y trail − y distACTD

(4.17)

where: y distACTD = VleadTAS ⋅ t spacing

(4.18)

The re-formulation of the ACTD spacing criterion as a constant distance criterion was used in the design and implementation of the guidance law controller. 4.2.2.2. Spacing Guidance The linear guidance law defined in sub-section 4.1.2.3. was used:

(

) (K s+ s )

Vcmd = K p ⋅ yerror ⋅ s 2 + 2ζ manωman ⋅ s + ωman ⋅ 2

i

(4.19)

4.2.2.3. Transmission Loss Compensation The basic guidance law presented above contains no compensation for imperfect ADS-B messages but was used to find the minimum level of performance. The following compensation terms were then added to the guidance law for update rate, latency and accuracy to cope with a realistic imperfect ADS-B transmission model. •

Update Rate

In the basic guidance law, the lead position was assumed constant between ADS-B updates. An update rate compensation term was designed containing the following extrapolation, to better approximate the lead’s position between consecutive ADS-B updates: y lead _ predicted (t + τ ) = y lead (t ) + VleadTAS (t ) ⋅τ

(4.20)

where τ was the time between two updates. The above was found to be a good estimate of the lead aircraft intermediate trajectory when the true air speed (TAS) was varying slowly. •

Latency

Compensation was added for only the constant delay component of latency. The constant delay component could be calculated if the time measurement was assumed to be included in the ADS-B report. To do this, the guidance law was slightly adjusted: the control error term contained a third parameter which was a correction depending on the amount of constant latency (tlat). y lead _ adjusted (t ) = y lead (t ) + VleadTAS (t ) ⋅ t lat

•

(4.21)

Accuracy

Accuracy of the surveillance information was modelled using white noise characterised by a mean and deviation. A first order Butterworth filter was added to the speed to compensate for lack of accuracy and hence improve the constant time delay spacing performance. The band pass of this filter was between 0.01 rad/s and 1 rad/s and was chosen so that the bandwidth of air speed control was centred in this interval. 14



5.

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APPARATUS

5.1. FAST-TIME SIMULATION ENVIRONMENT All aircraft models, spacing guidance, wind and atmosphere models were simulated in MATLAB (version 6, release 12) and Simulink (version 4, release 12) for Windows/Personal Computer (PC). All the fast-time simulations were performed on a Pentium IV based PC (1.5 GigaHertz, with 256 MegaBytes internal Random Access Memory (RAM)). MATLAB is a high-performance language tool for technical computing. It integrates programming, computation and visualisation in an easy-to-use environment. MATLAB consists of five main parts: • • • • •

The MATLAB language (a high level language for technical computing). The MATLAB working environment (set of tools to facilitate the work of user or programmer). Handle graphics (high-level commands for 2-D and 3-D data visualization). The MATLAB mathematical function library (large collection of computational algorithms). The MATLAB Application Program Interface (allows interaction with C and Fortran).

Simulink is a widely used software package for modelling, simulating and analysing dynamic systems. It supports linear and non-linear systems, modelled in continuous or sampled time. Simulink provides a Graphical User Interface (GUI) for building models as block diagrams, using click-and-drag mouse operations. MATLAB and Simulink are integrated so the models can be simulated and revised in either environment at any point. 5.2. ADS-B MODEL Each leading aircraft sends a subset of ADS-B surveillance information: position, velocity, position accuracy, velocity accuracy (RTCA Navigation Accuracy Categories) and a time of measurement. This state vector information was transmitted through ADS-B reports. The ADS-B model was composed of an ADS-B transmitter model and ADS-B receiver model. To simulate more realistic (i.e. imperfect) ADS-B transmissions, the following characteristics were modelled: •

Update rate of reports: initially, a perfect update rate was considered, i.e. the probability of reception was 100 %, then probabilities of reception less than 100 % were considered.

•

Latency of transmission: assumed to be the same for all information. Furthermore, this latency consisted of a mean time delay with a stochastic variation (to model jitter). The standard deviation of this stochastic variation determined the amount of jitter.

•

Accuracy of surveillance information: accuracy of the horizontal position and velocity was characterised by gaussian distributions with zero means and respective standard deviations sigma σhp and σhv (Table 2).


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Table 2: Navigation Accuracy Categories (NAC)- Position and Velocity (RTCA, 2002)

NAC for Position

Horizontal. Position Error (2σhp ≈95 %)

NAC for Velocity

Horizontal Velocity Error (2σhv≈95 %)

0

Unknown

0

Unknown

1

< 10 NM

1

< 10 m/s

…

…

2

< 3 m/s

7

< 0.1 NM

3

< 1 m/s

8

< 0.05 NM

4

< 0.3 m/s

9

< 30 m

10

< 10 m

11

50 NM), aircraft N˚ 2 can be initialised closer to the waypoint than aircraft N˚ 1 even though this may not be operationally desirable. Figure 17 shows how the minimum and maximum admissible entry condition bounds for aircraft N˚ 2 vary with the difference in initial speed. The speed of aircraft N˚ 1 was constant at 272 knots CAS.


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The horizontal axis represents the values corresponding to the speed difference between the two aircraft. The vertical axis represents the initial spacing error between the two aircraft.

Figure 17: Entry conditions – initial spacing error for a sequence of two aircraft having different initial speeds

The centre of the graph corresponds to ideal entry conditions of aircraft N˚ 2: at 8 NM behind aircraft N˚ 1 and with the same speed. The surrounding speed-distance contours show the entry condition bounds of aircraft N˚ 2 corresponding to initial distances of aircraft N˚ 1 to the merge waypoint. The results show that the impact of varying the speed was slight. For speed variation between -60 and +50 knots, the distance/speed gradient was approximately 1 NM per 20 knots. 7.1.3.2. Sequence of Three Aircraft Figure 18 shows the admissible entry condition bounds of initial spacing error for a sequence of three aircraft with equal initial speeds. The results are presented as contour maps, where each contour corresponds to an initial distance of aircraft N˚ 1 from the merge waypoint. The horizontal axis represents the initial spacing error between aircraft N˚ 2 and aircraft N˚ 1. The vertical axis shows the initial spacing error between aircraft N˚ 3 and aircraft N˚ 2.

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Each contour defines a region inside which the entry conditions satisfy the merging criteria.

Figure 18: a) Entry conditions – initial spacing error for a sequence of three aircraft with the same speeds b) Initial position for a sequence of three aircraft with the same speeds

The contours of the map show how the initial spacing error envelopes of the second and third aircraft grew with initial distance of aircraft N˚ 1 from the merge waypoint. Example 2: if aircraft N˚ 1 is initialised at 70 NM from the waypoint, and aircraft N˚ 2 is initialised with a spacing error of –10 NM, then aircraft N˚ 3 can be initialised with a spacing error anywhere between –5 NM and + 23 NM relative to aircraft N˚ 2. Irregularities in the shape contours correspond to limiting cases, when aircraft N˚ 2 was initialised with the maximum spacing error and consequently the spacing or speed criteria could not be met for certain special intermediate initial positions of aircraft N˚ 3. Figure 18 b) presents the same results as in Figure 18 a), but in terms of absolute admissible entry position bounds with respect to the merge waypoint. Example 3: if aircraft N˚ 1 is initialised at 70 NM from the waypoint, and aircraft N˚ 2 is initialised at 68 NM from the waypoint then aircraft N˚ 3 can be initialised anywhere between 71 NM and 99 NM from the waypoint.


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7.2. ‘REMAIN’ EXPERIMENTS 7.2.1. Effect of ADS-B Transmission Quality 7.2.1.1. Ideal ADS-B Reference Results The reference results from trials described in section 6.2.1 represent in-trail airborne spacing performances with perfect ADS-B, i.e. a continuous update rate, no latency and 100 % accuracy. The speed variation was 22 knots for the CD spacing criterion and 3 knots for the ACTD spacing criterion. Maximum spacing error was ~0.03 NM, (i.e. 0.4 %) for the CD spacing criterion and ~4 s, (i.e. 6 %) for the ACTD spacing criterion. The engine throttle behaviour was smooth, without oscillations. 7.2.1.2. ADS-B Update Rate Sixty trials for constant update periods between 0.1 s and 30.0 s were performed as described in section 6.2.1.3. Figure 19 shows the maximum spacing error as a function of update period for basic CD and ACTD spacing criteria (solid lines), and update rate compensated CD and ACTD spacing criteria (dotted lines). The update periods when the trailing aircraft engine throttle oscillations reached a peak of 20 % are also shown.

Figure 19: Impact of ADS-B update rate on in-trail performances: maximum spacing error (%) as function of update period. Solid lines: basic guidance law, dashed lines: guidance with update rate compensation.

For small update periods (up to 5 s), the in-trail aircraft behaviour was stable and spacing error remained small (below 10 % for both spacing criteria). A significant degradation in spacing error was observed for update periods greater than 6 s for the CD spacing criterion. Using the ACTD spacing criterion, the error and speed range as a function of the update period had very shallow, almost linear gradients for update periods up to 12 s. Note that for small update periods (between 0.1 and 4 s) the CD spacing criterion resulted in smaller errors than the ACTD spacing criterion. During the operational scenario, the maximum CAS variation for the trailing aircraft was also calculated. Again, the ACTD spacing criterion exhibited better behaviour: smaller speed variations were required (less than 10 knots CAS for update periods up to 10 s), due to the trailing aircraft trying to closely follow the air speed profile of the leading aircraft as a function of position. The speed variation was considerable for the CD spacing criterion (~ 40 knots CAS for update periods up to 5 s). 32



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With the update rate compensated spacing guidance (using Equation (4.20)), engine throttle oscillations were attenuated and therefore the in-trail performances (Figure 19, dotted lines) were stable for larger updates (up to 12 s for the CD spacing criterion and up to 25 s for the ACTD spacing criterion). 7.2.1.3. ADS-B Update Rate with Probability of Reception Two thousand four hundred Monte-Carlo trials for various update rates of probability of reception were performed. As probability of reception decreased, engine throttle oscillations increased, but the spacing error was always maintained below 10 % (Figure 20). When engine throttle oscillations exceeded 100 %, the autopilot saturated and the spacing could no longer be maintained. ACTD spacing criterion exhibited better performance and resilience to lost messages than the CD spacing criterion.

Figure 20: Engine throttle oscillations (%) function of probability of reception of ADS-B, guidance with update rate compensation

7.2.1.4. ADS-B Latency Seventy-two trials for various constant latencies and 4800 Monte-Carlo trials for various jitter delays were performed described in section 6.2.1.3. Figure 21 and Figure 22 show how maximum spacing error increased with increasing latency for the ACTD and CD spacing criteria. The figures show how jitter degrades spacing performance of both spacing criteria. The figures show also how the compensation for latency significantly improved the spacing performance stability.

Figure 21: Impact of ADS-B latency on spacing error, basic guidance law


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For constant delays, below 17 s, the CD spacing criterion performs slightly better than the ACTD spacing criterion. Both criteria had very shallow spacing error gradients. Above 17 s, the ACTD spacing criterion remains robust, but the CD becomes unstable. The same behaviour was obtained in the presence of jitter, but the CD spacing criterion instability occurred at 5 s of latency without compensation and at 6 s with compensation. The ACTD spacing criterion became unstable for larger values of latency (7 s without compensation, and 10 s with compensation). Note that the maximum possible stochastic deviation was considered, but for a real system, the jitter is expected to be smaller. The speed variation was large for the CD spacing criterion (~40 knots CAS variation for up to 7 s of latency), but very small for ACTD spacing criterion (~10 knots of variation for latency up to 17 s). For all trials, the engine throttle was less affected than in those of the update rate analysis (below 20 % of oscillations) therefore throttle behaviour was not presented here.

Figure 22: Impact of ADS-B latency on spacing error, guidance with latency compensation

7.2.1.5. ADS-B Accuracy Two thousand five hundred Monte-Carlo trials for various accuracy levels of position and velocity were performed as described in section 6.2.1.3. As the accuracy in position and velocity decreased, engine throttle oscillations increased, but spacing error was always maintained below 8 % (Figure 23). When engine throttle oscillations exceeded 100 %, the autopilot saturated and the spacing could no longer be maintained. Figure 23 shows how maximum throttle oscillations varied with horizontal position error. Vertical lines mark the standard RTCA NACp (position) categories. The CD spacing criterion was stable up to NACp =7 (0.1 NM) with a maximum speed range difference of 35 knots, whereas the ACTD spacing criterion was unstable unless the speed accuracy was better than NACv=4 (0.3 m/s). The CD spacing criterion depended only on lead position, whereas the ACTD spacing criterion depended on both lead position and speed.

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Figure 23: Impact of ADS-B accuracy on in-trail spacing performance. Solid line: throttle oscillations as function of position accuracy (CD), dotted lines: throttle oscillations as function of position and velocity accuracy (ACTD), basic guidance law.

This could explain the poor performance of the ACTD spacing criterion for inaccurate ADS-B speed reports and hence the improvement when speed was filtered (Figure 24). The ACTD spacing criterion was stable for NACv=4 and 3 (less than 30 % thrust oscillations). These results were obtained with a simple filter design. It is expected that the use of an advanced filter could increase the spacing performance.

Figure 24: Impact of ADS-B accuracy on in-trail performance. Solid line: throttle oscillations function of position accuracy (CD), dotted lines: throttle oscillations function of position and velocity accuracy (ACTD), guidance with accuracy compensation.


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7.2.2. Evaluation of Time-Based Spacing Criteria Figure 25 and Figure 26 show the true airspeed behaviour of both spacing criteria trials described in section 6.2.2. Using the ACTD criterion a non-optimal situation appeared, when the lead aircraft decreased speed. According to the time based concept, the trailing aircraft would have to decelerate too. However, using the ACTD criterion (i.e. equation (4.17)), a reduction of the lead aircraft speed resulted in an increase in the criterion error. Therefore, the trailing aircraft had to reduce the error by increasing speed. Consequently, the trailing aircraft accelerated initially and then decelerated in order to match the lead aircraft speed profile. This speed peak is clearly shown in Figure 25.

Figure 25: ACTD - true airspeed behaviour

Figure 26: CTD - true airspeed behaviour

Concerning the time spacing error (Figure 27), the CTD criterion produced smaller error (1.5 s) than the ACTD criterion (6.5 s).

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Figure 27: CTD and ACTD, time spacing error comparison for a continuous update rate

7.2.3. Effect of Mixed Aircraft Types and Varying Wind Conditions Figure 28 to Figure 33 show the maximum and mean time delay spacing errors for mixed aircraft types in cross, head and tail winds (constant and turbulent) described in section 6.2.3. Each graph contains the four combinations of heavy and light aircraft in a sequence. 7.2.3.1. Constant Wind For constant wind, spacing errors were small for all wind directions. The spacing was maintained with all spacing errors less than 8 s up to maximum constant wind speeds of 80 knots at 30 feet AGL (i.e. a wind speed of 212 knots at 29,000 feet). The spacing error was always greatest when a heavy aircraft followed a light. This effect was strongest in cross wind, where the spacing error was at least 60 % more than the other three combinations of aircraft type. With regard to wind direction, cross and head winds had a stronger impact on time spacing error than tail wind. For a heavy aircraft following a light in a maximum constant wind of 80 knots at 30 feet AGL, cross wind maximum spacing error was 7.5 s, head wind maximum spacing error was 7 s and tail wind maximum spacing error was 4 s. Maximum spacing error increased slowly with cross and head wind speeds, whereas for light tail winds (up to 20 knots at 30 feet AGL) the trend was for maximum spacing error to decrease, particularly for a heavy aircraft following a light. 7.2.3.2. Turbulent Wind Sixteen thousand Monte Carlo trials for various turbulent wind speeds and directions were performed as described in section 6.2.3. For each combination of wind speed and direction 500 random trials were run. The maximum time delay spacing error encountered during each trial was recorded. The largest values of these 500 maximum spacing errors are shown in Figure 28, Figure 29 and Figure 30 for cross, head and tail winds respectively. To validate these extreme values the corresponding mean values of the 500 maximum spacing errors are presented in Figure 31, Figure 32, and Figure 33 for the three wind directions. Project AGC-Z-FR-0000 - EEC Report No. 391

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All six graphs for turbulent winds show a significant increase in maximum spacing error over the corresponding constant wind conditions. For all constant winds, the spacing error was maintained below 8 s whereas turbulent cross and head winds produced maximum spacing errors of 60 s before reaching maximum strength considered in the study. The ‘largest of maximum spacing errors graphs’ and ‘mean of maximum spacing errors graphs’ have similar forms indicating that the extreme values were well behaved, and that the number of trials was sufficient. Note that the ratio of two between the vertical axes of the two types of graphs implies that the largest of maximum spacing errors was about twice the mean. As with constant winds, the maximum spacing error increased with cross and head wind speeds, whereas for light tail winds (up to 20 knots at 30 feet AGL) the trend was for maximum spacing error to decrease slightly, particularly for a heavy aircraft following a light.

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Figure 28: Turbulent cross wind effects on largest of maximum spacing errors

Figure 31: Turbulent cross wind effects on mean of maximum spacing errors

Figure 29: Turbulent head wind effects on largest of maximum spacing errors

Figure 32: Turbulent head wind effects on mean of maximum spacing errors

Figure 30: Turbulent tail wind effects on largest of maximum spacing errors


Figure 33: Turbulent tail wind effects on mean of maximum spacing errors

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7.2.3.3. Along-Track Winds When considering the significant reduction in maximum time spacing error for tail winds relative to head winds it can be useful to make a comparison with the equivalent distance spacing errors. For a given altitude and calibrated airspeed an aircraft has a higher ground speed in a tail wind than a head wind. Therefore a distance spacing error corresponds to a shorter time spacing error in a tail wind than a head wind. Tail winds may significantly decrease the maximum time spacing error relative to head winds but because aircraft ground speed can be significantly higher in tail winds the corresponding maximum spacing errors may be very similar using an equivalent distance based criterion. For example Figure 34 a) and b) show this difference for constant head and tail winds of 70 knots. The example concerns constant winds, but the same rationale could be used to explain the results for turbulent winds.

Figure 34: a) Time spacing errors for constant head and tail winds b) Equivalent distance based spacing errors for constant head and tail winds

7.2.3.4. Cross-Track Winds The results show that cross-track winds (constant and turbulent) had just as much effect on alongtrack spacing performance as along-track winds. This confirms similar observations made in [Hoffman et. al, 2003a] where an explanation was suggested that the cross-track guidance became increasingly coupled with the along-track guidance for significant differences between track angle and heading angle i.e. strong cross winds. 40



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SUMMARY OF MAIN RESULTS

This report presented model based simulation results performed for two airborne spacing applications for sequencing aircraft: ‘Merge’ and ‘Remain’. •

In a ‘Merge’ application, two aircraft (a lead aircraft and a trail aircraft) are flying along converging trajectories. When the lead reaches the merging waypoint, the spacing between the aircraft must be within a tolerance of a constant desired spacing (in time or distance).

•

In a ‘Remain’ application two aircraft are flying along the same trajectory and a constant desired spacing (in time or distance), within a tolerance, should be maintained during the flight.

Table 8 summarises the current status of the fast-time experiments in context of potential further investigations. Table 8: Current status of the fast-time simulations: ‘Yes’ in the table means a performed study, ‘No’ means a potential future study High level objectives Prototyping of spacing guidance algorithms for the human-in-the-loop experiments

Increase understanding of intrinsic dynamics of sequences of aircraft under normal and extreme operating conditions

Evaluate effect of air-air surveillance transmission quality on the performances of airborne spacing

Specific objectives

Merge

Remain

Develop guidance laws

Yes

Yes

Develop spacing director system

Yes

No

Develop time based spacing criteria

No

Yes

Measure effect on spacing performance of initial position and speed

Yes

No

Measure effect on spacing performance of heavy and light aircraft types

No

Yes

Measure effect on spacing performance of constant and turbulent wind conditions

No

Yes

Measure effect on spacing performance of ADS-B update rate, latency and accuracy

No

Yes

8.1. SUMMARY OF ‘MERGE’ RESULTS Four spacing guidance laws for sequencing aircraft on merging trajectories were studied: a linear ‘distance-based’ guidance law, a linear ‘speed-based’ guidance law, a non-linear guidance law based on trajectory prediction and a linear guidance law based on lead groundspeed profile. A set of performance criteria was established along with metrics of spacing distance error and airspeed difference at the merging waypoint. To compare performance of different guidance laws, a set of operational scenarios was developed. The ‘distance-based’ linear guidance law produced both large spacing distance errors and large speed errors at the merging waypoint. The ‘speed based’ linear guidance law achieved near zero error spacing but with very large speed errors for all scenarios. The non-linear guidance law achieved the desired spacing with minor errors and with the similar aircraft speeds at the waypoint. However, this guidance law is more difficult to design for use (complex computations, long simulations requiring high memory resources) when aircraft are in descent. Project AGC-Z-FR-0000 - EEC Report No. 391

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The best performing guidance law was that based on the lead aircraft groundspeed. It was able to merge multiple aircraft in descent under turbulent wind conditions with near-zero error spacing and with the similar aircraft speeds. Therefore this guidance law, in the loop with a spacing director and a human pilot model, was selected for further studies. Effects of initial distance and speed on the ability of an aircraft to descend and establish a stable spacing (8 NM) behind another by a given merge waypoint were investigated. Results for two and three aircraft at the same initial speed show how the possible initial spacing error envelope grew from [-0, +0] to [-22, +25] NM when the initial distance of the lead aircraft to the merge waypoint was increased from 0 to 100 NM. The impact on initial spacing error of varying the difference in initial speed was slight. These results could contribute towards the development of a tool for helping controllers and pilots determine whether a particular ‘Merge’ operation is feasible. Future studies could investigate how other factors such as wind speed and turbulence, altitude and altitude rate, aircraft type and more aircraft in the sequence affect airborne spacing performance. In particular the admissible speed envelope model used in this study could be reduced to a level more comparable with normal operating limits to give more realistically conservative entry conditions. A natural extension of the current work would also be to develop time based spacing for the ‘Merge’ behind application to provide continuity with the already developed time based ‘Remain’ behind guidance. Effects of ADS-B transmission quality on ‘Merge’ behind could also be interesting to compare with those obtained for ‘Remain’ behind. 8.2. SUMMARY OF ‘REMAIN’ RESULTS The impact of ADS-B update rate, latency and accuracy on the airborne spacing performance of a sequence of six aircraft in descent was investigated using fast time simulation. Results show (Table 9) that maximum spacing error increased steadily with increasing update period and latency up to a certain point after which maximum spacing error increased rapidly with both update period and latency. This change in stability point was coincident with saturation of the throttle setting. Table 9: ‘Remain’: performance of improved spacing guidance for realistic ADS-B transmission Constant Distance Spacing Criterion

Approximate Constant Time Delay Spacing Criterion

Basic Guidance

Guidance with Transmission Loss Compensation

Basic Guidance

Guidance with Transmission Loss Compensation

Maximum Update Period

4.0 s

10.0 s

8.0 s

16.0 s

Maximum Latency (constant)

12.0 s

18.0 s

7.0 s

22.0 s

Maximum Latency (with jitter)

5.0 s

6.0 s

7.0 s

10.0 s

45 m /

45 m /

70 m /

80 m /

not used

Not used

0.1 m/s

1 m/s

Minimum Horizontal Position/Velocity Accuracy

Approximate constant time delay (ACTD) spacing remained more stable than constant distance (CD) spacing for degradations in update rate, latency and accuracy. CD spacing was sensitive to the quality of ADS-B aircraft position information whereas ACTD spacing was sensitive to the quality of ADS-B aircraft position and speed information (particularly speed accuracy). 42



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Transmission loss compensation terms for update rate, latency and accuracy produced significant improvements in spacing guidance stability. The performance of this improved guidance is summarised below, for a maximum 20 % spacing error and maximum 20 % of engine throttle oscillations (imperfections were only present in one of the transmission quality parameters at a time). In these ADS-B transmissions quality trials the effects of update rate, latency and accuracy were considered separately for a sequence of aircraft of the same type with no wind. Future work could include analysing the effect of varying all three ADS-B transmission quality parameters together for a sequence of aircraft of different types in changing wind conditions. An additional metric for measuring spacing guidance performance could be maximum closure rate between consecutive aircraft. These results were obtained using an approximation (ACTD) to constant time delay. It is suspected that using an exact definition of constant time delay would improve spacing performance particularly with regard to speed sensitivity. The effects of mixed aircraft types and wind conditions on the ability of an aircraft to maintain a constant time delay along-track spacing behind a descending lead aircraft were investigated. An exact constant time delay (60 s) spacing criterion based on lead aircraft position history was used to compare the spacing performance of all combinations of heavy and light aircraft for different wind conditions. Results show for both constant and turbulent winds that cross-track winds could have just as detrimental an effect on along-track time based spacing performance as along-track winds. Turbulent winds severely degraded time based spacing stability particularly in cross and head wind conditions. Tail winds were the least disturbing for time based spacing in both constant and turbulent winds. A heavy aircraft following a light tended to produce the maximum spacing errors. The other three combinations of aircraft type resulted in similar maximum spacing error behaviour. The effect of mixed aircraft types on airborne spacing in aircraft pairs could be extended to chains of aircraft with different time spacing. Compounding effects of airborne surveillance transmission quality such as update rate, latency and accuracy could also be investigated.


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9.


CONCLUSIONS

This report presented the results of the aircraft guidance model based experiments conducted in 2002 and 2003. These experiments fitted in with a series of human-in-the-loop and model based validation exercises aimed at investigating the use of spacing instructions (denoted airborne spacing) for sequencing of arrival flows. The human-in-the-loop experiments (pilots and controllers) enabled an understanding of the impact of airborne spacing on human activity, effectiveness and safety. The model based experiments were intended to complement the validation of airborne spacing by enabling prototyping as well as collecting a large quantities of data under a variety of conditions. A series of model-based experiments was conducted using MATLAB/Simulink, with essentially an air perspective. The results presented in the report are consistent with the hypothesis that the airborne spacing ‘Merge’ and ‘Remain’ applications should be robust under normal conditions. The results based on extreme conditions may be useful to evaluate the limits of applicability for ‘Merge’ and ‘Remain’ applications. 9.1. FUTURE EXPERIMENTS Data collected from future human-in-the-loop experiments could be used to improve human pilot and spacing director models. In turn, the subsequent model based simulations should improve the spacing assistance provided to the flight crew for the pilot-in-the-loop simulations. With model based experiments the potential exists to study safety aspects by observing the impact of varying several parameters in a large number of trials (MonteCarlo simulations). Eventual interaction with an ACAS Airborne Collision Avoidance System needs investigation, especially for merge situations.

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COSPACE 2003 Résultats d’Expérimentations avec Modèles de Guidage d’Avions

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TRADUCTION EN LANGUE FRANÇAISE SYNTHESE Ce rapport présente les résultats des expérimentations avec modèles de guidage d’avions, conduites au Centre Expérimental Eurocontrol en 2002 et 2003. Ces expérimentations s’insèrent dans une série d’exercices de validation, avec humain dans la boucle et avec modèles, visant à étudier l’utilisation d’instructions d’espacement pour le séquencement des flux d’arrivées. Les expérimentations avec humain dans la boucle (pilote et contrôleur) permettent d’étudier l’impact des instructions d’espacement sur l’activité humaine, l’efficacité et la sécurité. Les expérimentations avec modèles visent à compléter la validation de l’utilisation d’instructions d’espacement en permettant le prototypage, mais aussi l’obtention de grandes quantités de données sous des conditions variées. Une série d’expérimentations avec modèles est conduite en utilisant MATLAB/Simulink, en ayant essentiellement une perspective bord. Une première expérimentation a permis de comprendre l’impact des profils de vitesse et d’altitude. Les expérimentations suivantes décrites dans ce rapport visent à étendre le cadre de la première expérimentation, avec pour but de : (i)

permettre le prototypage d’algorithmes de guidage d’espacement (en distance et en temps) pour les expérimentations avec humain dans la boucle ;

(ii)

comprendre la dynamique intrinsèque d’une séquence d’avions sous conditions opérationnelles normales et extrêmes (positions et vitesses initiales, turbulence de vent, types avion) ;

(iii)

évaluer la qualité de la transmission de surveillance air-air (taux de rafraîchissement, latence et précision de l’ADS-B, Automatic Dependent Surveillance Broadcast) sur les performances du maintien d’espacement.

Deux applications complémentaires de maintien d’espacement ont été étudiées : « Merge » (avions sur trajectoires convergentes) et « Remain » (avions sur même trajectoire). Cependant, comme il n’a pas été possible d’étudier toutes les combinaisons entre ces objectifs et les deux applications, il a été décidé de se concentrer sur des cas représentatifs: (i)

Quatre lois de guidage pour le séquencement d’avions sur trajectoires convergentes ont été étudiées : une loi linéaire basée sur la différence des distances, une loi linéaire basée sur la différence des vitesses, une loi non-linéaire basée sur une prédiction des trajectoires et une loi de guidage linéaire basée sur le profil de vitesse de l’avion cible. Pour évaluer ces quatre lois, un ensemble de critères de performance a été défini avec plusieurs scénarios opérationnels. La loi linéaire basée sur les distances a produit des erreurs importantes d’espacement et de vitesse. La loi linéaire basée sur les vitesses a produit une erreur d’espacement proche de zéro, mais avec un écart important entre les vitesses. La loi de guidage non-linéaire réalise une erreur d’espacement très faible avec des vitesses presque similaires, mais elle s’avère difficile à développer (calculs complexes, simulations longues et coûteuses en mémoire) pour le cas où les avions sont en descente. Parmi les quatre lois, celle basée sur la vitesse de l’avion cible a été la plus performante et la plus robuste. Ce guidage permet de gérer la convergence de plusieurs avions en descente dans des conditions de vent très perturbées, et couplé à un directeur d’espacement et à un modèle de pilote a été utilisé pour les deux objectifs suivants :

Projet AGC-Z-FR-0000 - Rapport CEE n° 391

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(ii)

Une première expérimentation a été menée pour étudier l’effet des conditions d’entrées (distances et vitesses initiales). Il s’agissait d’étudier la capacité, pour un avion sujet en descente de 29000 à 3000 pieds, d’établir un espacement stable de 8 NM derrière un avion cible à un point fixé. Les résultats avec deux et trois avions ont montré comment l’enveloppe de l’erreur d’espacement au point varie lorsque la distance initiale entre le premier avion et le point évolue. L’impact de la différence de vitesses initiales sur l’erreur d’espacement au point est limité. L’effet du type avion et du vent sur la capacité d’un avion sujet à maintenir un espacement constant (en temps) derrière un avion cible en descente a été ensuite analysé. Un critère exact d’espacement en temps utilisant les positions passées de l’avion cible a été employé afin de comparer les performances d’espacement pour toutes les combinaisons entre un avion lourd et un avion léger dans une séquence avec des conditions différentes de vent (avec ou sans turbulences). Les résultats ont montré que les vents latéraux (« cross-winds ») et le long de la trajectoire (« along-track winds ») ont le même effet négatif sur l’espacement en temps tant pour le vent constant que turbulent. Les turbulences dégradent la stabilité de l’espacement en temps, notamment pour le vent de face (« head wind ») et latéral. Les vents de dos (« tail winds ») ont eu un effet négatif plus faible sur l’espacement. En considérant les différents types avion, il a été établi qu’un avion lourd qui suit un avion léger provoque l’erreur d’espacement la plus importante. Les trois autres combinaisons possibles ont montré des erreurs maximales d’espacement similaires entre elles mais moins importantes.

(iii)

L’effet de la qualité de la transmission ADS-B sur les performances d’espacement a été étudié pour une séquence de six avions. Les résultats ont montré que pour des dégradations concernant le taux de rafraîchissement, la latence et la précision, l’espacement en temps s’avère plus stable que l’espacement en distance. Des termes compensateurs ont été introduits dans la loi de guidage, permettant de retrouver des performances comparables au cas sans dégradation de la transmission.

Conclusion et futures expérimentations L’étude des conditions opérationnelles normales a permis de valider la robustesse des applications étudiées, « Merge » et « Remain ». L’étude des conditions opérationnelles extrêmes a permis de cerner leurs limites d’utilisation. Les données obtenues lors des prochaines expérimentations avec pilotes dans la boucle seront utilisées pour améliorer le modèle pilote. En retour, un modèle plus réaliste permettra d’améliorer l’assistance d’espacement fourni aux équipages pour les expérimentations avec humain dans la boucle. De plus, cela permettra d’étudier l’impact des paramètres introduits (ou d’autres paramètres) par un très grand nombre de cas (simulations de type MonteCarlo) par exemple à des fins d’analyse de sécurité. L’interaction éventuelle avec l’ACAS, Airborne Collision Avoidance System, devra être étudiée notamment dans les situations de convergence.

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Introduction Ce rapport présente les résultats des expérimentations avec modèles de guidage d’avions, conduites au Centre Expérimental Eurocontrol en 2002 et 2003. Ces expérimentations s’insèrent dans une série d’exercices de validation, avec humain dans la boucle et avec modèles, visant à étudier l’utilisation d’instructions d’espacement pour le séquencement des flux d’arrivées. Les expérimentations avec humain dans la boucle (pilote et contrôleur) permettent d’étudier l’impact des instructions d’espacement sur l’activité humaine, l’efficacité et la sécurité. Les expérimentations avec modèles visent à compléter la validation de l’utilisation d’instructions d’espacement en permettant le prototypage, mais aussi l’obtention de grandes quantités de données sous des conditions variées. Une série d’expérimentations avec modèles est conduite en utilisant MATLAB/Simulink, en ayant essentiellement une perspective bord. Une première expérimentation a permis de comprendre l’impact des profils de vitesse et d’altitude. Les expérimentations suivantes décrites dans ce rapport visent à étendre le cadre de la première expérimentation, avec pour but de : (i)

permettre le prototypage d’algorithmes de guidage d’espacement pour les expérimentations avec humain dans la boucle ;

(ii)

comprendre la dynamique intrinsèque d’une séquence d’avions sous conditions opérationnelles normales et extrêmes (positions et vitesses initiales, turbulence de vent, types avion) ;

(iii)

évaluer la qualité de la transmission de surveillance air-air (taux de rafraîchissement, latence et précision de l’ADS-B) sur les performances du maintien d’espacement.

Le document est organisé comme suit : •

Section 2 introduit les principes, les notions de base et le contexte du projet.

•

Section 3 présente les objectifs principaux de cette étude et décrit le contexte opérationnel : les applications d’espacement « Merge » et « Remain » et la qualité de la transmission ADS-B.

•

Section 4 décrit les lois de guidage correspondantes.

•

Section 5 décrit l'environnement de simulation : le modèle avion, le modèle ADS-B, les modèles d'atmosphère et de vent et la plate-forme MATLAB/Simulink.

•

Section 6 donne la procédure de préparation et de mise en oeuvre de l’expérimentation.

•

Section 7 présente les résultats détaillés.

•

Section 8 récapitule les résultats principaux de l’expérimentation et propose des recommandations.

•

Section 9 présente les conclusions.

•

Traduction en langue française de la synthèse, de l’introduction, des objectifs, du sommaire de résultats et de la conclusion.

•

Annexe avec la validation du modèle avion.


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Objectifs Les objectifs de haut niveau introduits précédemment seront déclinés en objectifs spécifiques, pour chacune des deux applications, comme suit :

Objectifs de haut niveau

Permettre le prototypage d‘algorithmes de guidage d’espacement (en distance et en temps) pour les expérimentations avec humain dans la boucle

Comprendre la dynamique intrinsèque d’une séquence d’avions sous conditions opérationnelles normales et extrêmes

Evaluer la qualité de la transmission de surveillance air-air sur les performances du maintien d’espacement

Objectifs spécifiques

y

Lois de guidage (« Remain » et « Merge »)

y

Directeur d’espacement (« Merge »)

y

Critères d’espacement en temps (« Merge » et « Remain »)

y

Effet de la position et de la distance initiale (« Merge ») Type avion (« Remain ») Vent constant et turbulent (« Remain »)

y y

y

Taux de rafraîchissement, latence et précision de la transmission ADS-B (« Remain »)

Conclusions, recommandations et expérimentations futures L’objectif de ce document a été de présenter les résultats des expérimentations avec modèles de guidage d‘avions, conduites au Centre Expérimental Eurocontrol en 2002 et 2003. Ces études ont eu pour but de compléter et valider les expérimentations en temps réel du projet CoSpace, en permettant le prototypage mais aussi l’obtention de quantités importantes de données sous des conditions variées. Les expérimentations, essentiellement côté bord on été effectuées dans l’environnement de MATLAB/Simulink. L’étude des conditions opérationnelles normales a permis de valider la robustesse des applications étudiées, « Merge » et « Remain ». L’étude des conditions opérationnelles extrêmes a permis de cerner leurs limites d’utilisation.

Recommandations En fonction des objectifs, plusieures recommandations peuvent être proposées : a)

permettre le prototypage d’algorithmes de guidage d’espacement pour les expérimentations avec humain dans la boucle ;

Le développement d’un directeur d’espacement plus performant permettra d’améliorer l’assistance d’espacement fourni aux équipages pour les expérimentations avec humain dans la boucle. b)

comprendre la dynamique intrinsèque d’une séquence d’avions sous conditions opérationnelles normales et extrêmes (conditions initiales, turbulence de vent, types avion) ;

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Les études à venir pourraient analyser l’impact d’autres facteurs (tels que l’altitude, l’enveloppe admissible de la vitesse, le nombre d’avions dans la chaîne) sur la performance d’espacement de séquencement des avions. Pour plus de réalisme, l’enveloppe admissible de la vitesse utilisée dans cette étude peut être réduite aux niveaux comparables aux limites opérationnelles. Une autre expérimentation pourrait être dédiée à l’étude d’espacement en temps pour l’application « Merge » pour montrer la continuité par rapport aux résultats déjà obtenus en temps pour « Remain ». c)

évaluer la qualité de la transmission de surveillance air-air (e.g. taux de rafraîchissement, latence et précision de l’ADS-B) sur les performances du maintien d’espacement.

Dans cette étude, les effets du taux de rafraîchissement, de la latence et de la précision ont été considéré séparément pour des conditions idéales de vol : absence de vent et séquences d’avions de même type. Un travail ultérieur sera de faire varier les trois paramètres à la fois, pour des séquences d’avions de différents types dans des conditions réalistes de vol (vent et turbulences). Les résultats acquis ont été obtenus en utilisant une approximation et non pas le critère exact d’espacement en temps basé sur un délai constant. On suppose que l’utilisation du critère exact peut améliorer les performances, notamment en ce qui concerne les oscillations en vitesse. L’impact de la qualité de la transmission ADS-B sur les performances d’espacement pour « Merge » peut aussi faire l’objet d’une étude ultérieure pour comparer les résultats avec ceux obtenus dans le cas du « Remain ».

Futures expérimentations Les données obtenues lors des prochaines expérimentations avec pilotes dans la boucle seront utilisées pour améliorer le modèle pilote. En retour, un modèle plus réaliste permettra d’améliorer l’assistance d’espacement fourni aux équipages pour les expérimentations avec humain dans la boucle. De plus, cela permettra d’étudier l’impact des paramètres introduits (ou d’autres paramètres) par un très grand nombre de cas (simulations de type MonteCarlo) par exemple à des fins d’analyse de sécurité. L’interaction éventuelle avec l’ACAS, Airborne Collision Avoidance System, devra être étudiée notamment dans les situations de convergence.


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ANNEX

Project AGC-Z-FR-0000 – EEC Report No. 391

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Aircraft Model Validation This annex contains an example operational scenario from the set used to validate the MATLAB/Simulink aircraft model. This scenario uses the NLR Fokker 100 simulation model as a reference. The NLR model applies the complete 6 degrees of freedom non-linear equations of motion and incorporates the non-linear aerodynamic model of the F28 mk100 aircraft as given in [Fokker, 1989] as well as the dynamic and static characteristics of the TAY 650 jet engine. This simulation package is level D qualified (conforms to JAR-STD 1A: Aeroplane Flight Simulators) which is the highest level of simulator performance. Validation Scenario Initial condition: Fokker 100, Mass 42,000 kg, 280 knots CAS, FL250, Level Flight. Directly after the start of the simulation a level change was commanded from FL250 to FL150, while keeping CAS constant. Simulation results are shown in figures A1 and A2 (where blue lines represent non-linear 6 degrees of freedom Fokker 100 evolution, and red lines represent the Matlab Fokker 100 model). As shown the airspeed and altitude profiles match very well. Note that during the initial descent the airspeed increases slightly above the target value. The reason for this behaviour is that during the descent at constant CAS the aircraft has to decelerate slightly in conformance with the TAS-CAS relationship with altitude. This is clearly visible in the time history of the longitudinal acceleration. Because the error signal driving the auto-throttle is composed of both a CAS and longitudinal acceleration components, the small positive bias in CAS is required to compensate for the constant deceleration. However, for this limiting case it has to be concluded that responses are well behaved, and typical for state of the art auto-flight systems. Some traces exhibit small biases, which can be explained. Firstly there is a small bias in the way vertical speed is controlled. The MATLAB model performs slightly better in maintaining the target vertical speed, but this can be expected from a simplified model, which for instance does not incorporate the pitching moment due to thrust increase. Secondly, there is a small bias in angle of attack (Alfa). There is no real requirement for accurate angle of attack simulation, and therefore a simplified model has been used within MATLAB, which can lead to some discrepancies. Furthermore there is a small bias in the bank angle. The MATLAB model flies exactly wings level at constant heading. The Fokker 100 model incorporates all kinds of asymmetric effects, such as asymmetric mass distribution and gyroscopic effects, due to which the aircraft requires a small sideslip and associated bank angle for constant heading flight. It appears that at idle thrust the fuel flow is slightly underestimated. Accurate modelling of idle power and associated fuel can be very complex due to many non-linearities in this regime, and effects of off-takes (bleed-air). Within MATLAB model the fuel flow is modelled simply as a linear function of delivered thrust. Therefore, while distance covered during the level change corresponds very well (43.3 NM versus 43.9 NM for respectively the NLR Fokker 100 and the AMAAI model) the fuel consumption shows some discrepancy (116 kg versus 86 kg).


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Validation scenario, Fokker 100 4

x 10

2.6

Altitude

CAS

286 CAS (kt) 284

2.4 2.2 H [ft]

282

2

280

1.8

278

1.6 1.4

0

100

200

300

400 500

276

0

100

Vert. speed

500

200

400

500

300 400

500

300

Mach

0.68 0.66

0

0.6 Mach [--] 0.62 0.6

V/S [ft/min] -500 -1000

0.58 -1500

0.56

-2000 0

100

200

300

400

500

0.54

0

100

Loadfactor

1.1

Lon. Accel

1 Vdot [kt/s]

1.05

200

0.5

Nz [g] 1 0 0.95 -0.5

0.9 0.85

0 100

200 300 400 time (sec)

500

-1

0

100

200 300 400 time (sec)

500

Figure A-1: Simulation results check-case

(Blue lines: non-linear 6 degrees of freedom Fokker 100, Red lines: Matlab Fokker 100 model)

54



EUROCONTROL

Validation scenario, Fokker 100

Alfa

Bank angle Phi [deg]

1.5

0.5

Alfa [deg]

0 1 -0.5

0.5

-1

0 0

100

200

300

400

500

0

100

Flightpath angle

200

300

400

500

Fuelflow

0.5

0.6

0 Gamma [deg] -0.5

0.5 FF [kg/s] 0.4

-1

0.3

-1.5 0.2

-2 -2.5

0.1 0

100

200

300

400

0

500

100 4

Pitch angle 1.5

3 T [N]

1

2.5

Theta [deg] 0.5

x 10

200

300

400

500

400

500

Thrust

2

0 1.5

- 0.5

1

-1 - 1.5

0

100

200

300

400

500

0.5

0

time (sec)

100

200 300 time (sec)

Figure A-2: Simulation results check-case

(Blue lines: non-linear 6 degrees of freedom Fokker 100, Red lines: Matlab Fokker 100 model) Project AGC-Z-FR-0000 – EEC Report No. 391

55