Fuzzy Logic Simulation for Automatic Speed Control System Pajarla Saiteja1 1
Assistant Professor, St M arys Group of Institutions, Hyderabad, INDIA
[email protected]
Abstract. Automatic Speed Control technique explains automatic deceleration of a vehicle during sudden approaching of obstacles. This paper explains an Electromagnetic Braking System, which controlled by Fuzzy logic. In this Fuzzy Logic algorithm results are carried out for automatic speed control of the vehicle while sudden obstacles, using inputs and output parameters as a load, distance and Electric current, pressure required for the clutch and accelerator. As mentioned above vehicle load and obstacle distance calculated by the strain gauge and ultrasonic sensor. Fuzzy logic control system involves Fuzzification for appropriate inputs and outputs. This paper is carried out parametric study on the performance of various range of distance of the obstacles and control sy stem followed by the sensors. Then the signals send to the fuzzy control system for further fuzzification and involve in compute the outputs. This algorithm is encountered only for 3rd and 4th gear while running. This control system is used to overcome accidents occurred by human errors or obstacles approached sudden to the vehicles. This control system ensures the safety and comfort of the driver and passengers. Keywords: Braking System - ECU –Load sensor – Obstacle sensor - Fuzzy logic - LabVIEW Software.
1
Introduction
Electromagnetic braking system explains about deceleration of wheel by magnetic flux, which generated by the electric current. Electromagnetic brakes are frictionless brakes and working under the principle of electromagnetism [1]. This frictionless braking system leads to increase lifespan and reliability of brakes . The main advantage of electromagnetic braking system is less chance of wear and maintenances cost. Electromagnetic Braking system is one of the future technologies which can replace traditional mechanical braking systems. Electromagnetic braking system generates magnetic flux in the direction of perpendicular to the rotating wheel when pressure applied by the brake pedal. Generated magnetic flux passes opposite direction to the rotating wheel which leads to slows down the wheel rpm. This electromagnetic braking system ensures the safety and comfort of the driver and passengers . Electromagnetic braking system carries out effective results than traditional mechanical brakes.
2
Fuzzy logic techniques introduced by Zadesh in 1965. Fuzzy logic is a form of information that represents notions that cannot be defined precisely, but which depend upon their perspectives. Fuzzy logic techniques majorly used in machine control techniques which refer to the statistics that cannot be expressed as the "true" or "false" but maybe as "partially true". Genetic algorithms and neural networks perform similar to the fuzzy and these are alternative techniques of Fuzzy logic. The fuzzy logic control system is used to overcome human errors while sorting out a solution. This makes easier to mechanize tasks that are performed by humans.
2
Mathematical Calculations
2.1
Mathematical Calculation for Mechanical Braking Torque Torque τ = F x µ x R F = Force acting on wheel µ = coefficient of friction R = Radius of the wheel
Reaction Forces at Front & Rear wheels:
Fig. 1. Reaction Force Diagram
Let FA = Braking force provided by the front wheels = µ×RA. FB = Braking force provided by the rear wheels = µ×RB. ) RB = m.gcosα( ) RA = m.gcosα( RA = Reaction force at front wheels RB = Reaction force at rear wheels If vehicle moving on plain surface (α = 0): RA = m.g( 2.2
) RB = m.g(
)
Let’s apply same concept and formulas to Suzuki Ciaz vehicle. Overall Length OL = 4490mm Wheel Base WB = 2650mm Ground Clearance GC = 170mm Kerb Weight KW = 1115kg Gross Weight GS = 1595kg
3
Braking Torque Calculation for Suzuki Ciaz: At Maximum load condition Weight of the vehicle = 1600kg RA = 960 N RB = 640 N
Braking Torque at front wheel (A) = 169 N-M Braking Torque at rear wheel (B) = 44 N-M Braking Torque (τ) for diff weights:
2.3
Load 1100
Table 1 Torque (τ) at different weights Front Bτ 116
1150
122
31
1200
127
33
1250
132
34
1300
137
35
1350
143
37
1400
148
38
1450
153
39
1500
159
41
1550
164
43
1600
169
44
Rear Bτ 30
Apply This Mechanical Braking Torque values in Electromagnetic Braking Torque Formula:
Electromagnetic braking Torque (b):
b = σds2 Ap tdω( )2 i2 σ = Electrical conductivity = Distance between center of disc and center of gravity, m = Pole area, m2 = Disc thickness, m = Angular velocity, rad/sec = Permeability of air n = no of turns g = air gap, m i = Applied current, KA Considerations for electromagnetic braking torque σ = 9.579x107 ohm m-1 , d s = 25mm, g = 5mm, = 12.568x10-7 NA -2 , n = 23000 and disc rotating with 2300 rpm. ds Ap Td ω
4
While substituting braking torque, disc thickness, electrical conductivity, angular velocity, and no of turn values into the eq(1), electric current getting as final output. By these calculation we can tabulate required electric current values corresponding to the brake torque.
Load 1100 1150
Table 2. Required EM B current at different loads Front Bτ 1600 1650
1200
1684
858
1250
1717
857
1300
1749
884
1350
1787
909
1400
1818
921
1450
1848
933
1500
1884
957
1550
1914
980
1600
1944
991
Rear Bτ 818 832
Consideration for obstacle distance, the max distance between vehicle and obstacle is 20m and the minimum distance is 10m. This algorithm is encountered only for 3rd and 4th gear while running and this system does not work during 1st and 2nd gears. Hydraulic or pneumatic systems are used to apply pressures on clutch and accelerator for getting smooth braking action. Another consideration for clutch and accelerator is maximu m pressure is 4bar and minimum pressure is 2bar, which explained in Table 3. Table 3. Pre calculated fuzzy values Load 1100 1150 1200 1250 1300 1350 1400
Ab Distance 20
Front EM Bτ 800
10
1600
818
4
4
20
825
416
2
2
10
1650
832
4
4
20
842
429
2
2
10
1684
858
4
4
20
859
429
2
2
10
1717
857
4
4
20
875
442
2
2
10
1749
884
4
4
20
894
455
2
2
10
1787
909
4
4
20
909
461
2
2
Rear EM Bτ 409
Clutch Pr 2
Accelerator Pr 2
5
1450 1500 1550 1600
10
1818
921
4
4
20
924
467
2
2
10
1848
933
4
4
20
942
479
2
2
10
1884
957
4
4
20
957
490
2
2
10
1914
980
4
4
20
972
496
2
2
10
1944
991
4
4
Table 3 explains about Electric current (Amp) for appropriate vehicle conditions. At 1100N load if an obstacle comes at a 10m, we required 1600A current for front axle EMB system and 818A current for the rear axle EMB system to stop the vehicle. Meanwhile 4 bar pressure acted on clutch and accelerator for increasing engine, gearbox life span.
3
Appling Fuzzy Logic
3.1
Grouping of Fuzzy Values into Triangular membership function Table 4. Triangular membership function values
Front EM Bτ
Rear EM Bτ
800
409
Clutch Pr 2
Accle Pr 2
Load
min max
1944
991
4
4
1600
1144
582
2
2
500
20 10
572 1372
291 700
1
1
3
3
250 1350
15
800.800.1372
409.409.700
2.2.3
2.2.3 2.3.4 3.4.4
diff diff/2 max-(diff/2) Low M edium
800.1372.1944
409.700.991
2.3.4
High
1372.1944.1944
700.991.991
3.4.4
3.2
Ob Distance 10
1100
5
1100.1100.1350 1100.1350.1600 1350.1600.1600
Forming Into Sub Iterations Table 5. Sub Iterated Fuzzy Values for Low Low
1100
Ab Distance 20
Front EM Bτ 800
1150 1200
20 20
825 842
416 429
2 2
2 2
1250
20
859
429
2
2
1300
20
875
442
2
2
Load
Rear EM Bτ 409
Clutch Pr 2
Accelerator Pr 2
10.10.15 10.15.20 15.20.20
6 1350
Load 1100 1150 1200 1250 1300 1350
20
894
455
2
2
Table 6. Sub Iterated Fuzzy Values for Low M edium Ab Distance Front EM Bτ Rear EM Bτ Clutch Pr Accelerator Pr 20 800 409 2 2 10
1600
818
4
4
20
825
416
2
2
10
1650
832
4
4
20
842
429
2
2
10
1684
858
4
4
20
859
429
2
2
10
1717
857
4
4
20
875
442
2
2
10
1749
884
4
4
20
894
455
2
2
10
1787
909
4
4
Table 7. Sub Iterated Fuzzy Values for M edium Low Load 1100
Ab Distance 20
Front EM Bτ 800
1150 1200
20
825
416
2
2
20
842
429
2
2
1250
20
859
429
2
2
1300
20
875
442
2
2
1350
20
894
455
2
2
1400
20
909
461
2
2
1450
20
924
467
2
2
1500
20
942
479
2
2
1550
20
957
490
2
2
1600
20
972
496
2
2
Rear EM Bτ 409
Clutch Pr 2
Accelerator Pr 2
7
3.3
Forming into fuzzy sets
Fig. 2. . Tabulation of Fuzzy sets
4
Forming of Fuzzy Rules in LabVIEW Software
Fig. 3. Fuzzy rules in LabVIEW Software
5
Input/ Output Values For Triangular Membership Function In Labview Software
8
Fig. 4. Loading of input/output values in LabVIEW Software
6
Output Verification
Fig. 5. Output verification at load 1500 and obstacle distance 10m
9
6.1
Verification of manual calculated values to LabVIEW Fuzzy logic controller output
Manually Calculated values at load 1500N and obstacle distance 10M are: Current required at Front Axle EMB is 1884 AV, Current required at Rear Axle EMB is 957 AV, and pressure required at Clutch and Accelerator is 4bar. Fuzzy logic controller output values at 1500N load and 10M distance are: Current required for Front Axle EMB is 1372 AV, Current required for Rear Axle EMB is 700 AV, and Pressure required at Clutch and Accelerator is 3bar. Here Fuzzy output values are closer to the manually calculated values. By these results, we can conclude that Fuzzy Logic Control system will give good results in Automatic Speed controlling System.
Conclusion Fuzzy is a form of knowledge representation suitable for notions that cannot be d efined precisely, but which depend upon their contexts. And it used to find out the optimum solution. Genetic algorithms and neural networks perform similar to fuzzy and these are alternative techniques of Fuzzy logic. The fuzzy logic control system is used to overcome human errors while sorting out the solution. This makes it easier to mechanize tasks that are already successfully performed by humans. Automatic Speed Control technique explains automatic deceleration of a vehicle during sudden approaching of obstacles. This paper explains an Electromagnetic Braking System, which controlled by Fuzzy logic. In this Fuzzy Logic algorithm results are carried out for automatic speed control of the vehicle while sudden obstacles, using inputs and output parameters as a load, distance and Electric current, pressure required for the clutch and accelerator. Vehicle load and obstacle distance calculated by the strain gauge and ultrasonic sensor. Fuzzy logic control system involves Fuzzification for appropriate inputs and outputs. This paper is carried out parametric study on the performance of various range of distance of the obstacles and control system fo llowed by the sensors. Then the signals send to the fuzzy control system for further fuzzification and involve in compute the outputs. This algorithm is encountered only for 3rd and 4th gear while running. This control system is used to overcome accidents occurred by human errors or obstacles approached sudden to the vehicles. This control system ensures the safety and comfort of the driver and passengers.
References 1. P. Sai Teja and Dr Jeyanthi S, “Fuzzy Logic Simulation for Brake by Wire Control System in Lecture Notes in M echanical Engineering 2016, ISBN 978-981-10-1770-4.
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