ANTI LOCK BRAKING SYSTEM -PGCSE-292-Manas-Kumar-Maity-27611223003.pptx

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1. ANTI LOCK BRAKING SYSTEM USING FUZZY LOGIC Budge Budge Institute of Technology Name: Manas Kumar Maity Roll No: 27611223003 Registration No: 232760410010 Subject: Seminar Term Paper Leading To Project Subject Code: PGCSE292 Semester: 2 nd Semester 2023-24

2. INTRODUCTION OF ABS SYSTEM Anti-lock braking system  (ABS) is an automobile safety System that allows the wheels on a  motor vehicle to maintain tractive contact with the road surface according to driver inputs while braking , preventing the wheels from locking up (ceasing rotation) and avoiding uncontrolled skidding ABS generally offers improved vehicle control and decreases stopping distances on dry and slippery surfaces; however, on loose gravel or snow-covered surfaces, ABS can significantly increase braking distance, although still improving vehicle control

3. WORKING OF ABS SYSTEM ABS CONSIST OF Wheel speed sensors Hydraulic Modulator unit Electronic control unit Braking Device

4. FUZZY LOGIC Fuzzy logic  is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1. By contrast, in Boolean logic , the truth values of variables may only be 0 or 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. For Intelligent control systems like fuzzy control can be used in ABS control to emulate the qualitative aspects of human knowledge by rule-based algorithms

5. Mathematical Modeling Vehicle Modeling Wheel dynamics Model Jw = ( 𝜇 * Fn *r ) - Tb 1 𝜆 = ( V – rw ) / v 𝜆 = [ V ( 1- 𝜆 ) - r w ] / V 2 rb = Ff J s w(s) =[ 𝜇 Fn r ] – Tb(s) T.F 𝜆(s) { s + ( 𝜇Fn/ mv ) } = ( 𝜇Fn/ mv ) – ( r s w(s) / v) T.F Slip ratio V= 𝜇Fn/m

6. Brake Model Tb = 𝜇d * rb * Fb 3 Tire Model Tf = Ff * r Ff = Fn * 𝜇t Fn = Mg 𝜇 ( tire) = F(𝜆) Tb(s) = 𝜇d * rb * Fb (s) T.F Fb (s) rb 𝜇d + Js w(S) = Tf Tb – Torque by brake Fb - Brake Force 𝜇d - 𝜇 for disc rb – disc radiu s W= angular velocity 𝜇t = 𝜇 = friction coefficient b/w tire & road Fn = Normal Force W(s) = [ Fn 𝜇 r - 𝜇d rd Fb (s)] / Js

7. Block Diagram 1/ { s + 𝜇Fn/ mv } + - 1/ Js 1/ r 𝜇 rb 𝜇d + - Tf =𝜇 Fn r 𝜇Fn/ mv E2 E1 W(s) Fb (s) 𝜆(s) Torque of Tire o/p variable Slip ratio o/p Brake Force Js + + E3 1/ rmv rs /v Tf (s) Tf (s) Fn

8. Control system block diagram Fuzzy controller

9. ⦿ The optimal breaking pressure results from the defuzzification of the linguistic variable pressure . ⦿ Finally a three-step controller determines the position of the magnetic valves, whether the pressure should be increased, hold firm or decreased.

10. Wheel model FZ: Wheel load R: Wheel radius w: Angular wheel frequency v: Velocity of wheel center FL: Longitudinal force

11. ⦿ Calculating the wheel slip by ⦿ the longitudinal wheel force results in

12. ⦿ At the beginning of an uncontrolled full braking, the operating point starts at s = 0, then rises steeply and reaches a peak at s = s max . ⦿ After that, the wheel locks within a few milliseconds because of the declining friction coefficient characteristic which acts as a positive feedback. At this moment the wheel force remains constant at the low level of sliding friction. Steering is not possible any more. ⦿ Therefore a fast and accurate control system is required to keep wheel slips within the shaded area shown in Figure 1.

13. ⦿ Furthermore Figure 2 depicts the hydraulic unit including main brake cylinder, hydraulic lines and wheel brake cylinders. ⦿ By means of two magnetic two-way valves each wheel, braking pressure p i, j is modulated. ⦿ Three discrete conditions are possible: decrease pressure, hold pressure firm and increase pressure (up to main brake pressure level only). ⦿ Each valve is hydraulically connected to the main brake cylinder, to the wheel brake cylinders and to the recirculation.

14. CG: Center of gravitiy a x : Longitudinal acceleration w i,j : Angular wheel frequency HU: Hydraulic Unit p i,j : Wheel brake pressure i: l=left, r=right j: f=front, r=rear Figure 2

15. ⦿ The knowledge of the actual vehicle speed over ground is vital in order to calculate wheel slips correctly. ⦿ In this approach the speed estimation uses multi sensor data fusion that means several sensors measure vehicle speed independently and the estimator decides which sensor is most reliable. ⦿ Figure 3 represents the schematic structure of the fuzzy estimator. The signals of the four wheel speed sensors w i,j are used as well as the signal of the acceleration sensor a x .

16. Figure 3

17. ⦿ In the data pre-processing block the measured signals are filtered by a lowpass and the inputs for the fuzzy estimator are calculated. ⦿ Four wheels slip , and an acceleration value D v a are calculated. The applied formulas are:

18. ⦿ whereby a Offset is a correction value consisting of an offset and a road slope part. It is derived by comparing the measured acceleration with the derivative of the vehicle speed v Fuz . ⦿ v Fuz (k-1) is the estimated velocity of the previous cycle. ⦿ A time-delay of T is expressed by the term 1/z.

19. ⦿ The fuzzy estimator itself is divided into two parts. ⦿ The first (Logic 1) determines which wheel sensor is most reliable, and the second (Logic 2) decides about the reliability of the integral of the acceleration sensor, shown in Figure 4. ⦿ This cascade structure is chosen to reduce the number of rules.

20. Figure 4

21. ⦿ Starting at block “Logic 1" and “Logic 2" the crisp inputs are fuzzificated. Figure 5 shows the input-membership- functions (IMF) with four linguistic values ( Negative, Zero, Positive and Very Positive ) Figure 5

22. ⦿ The rule base consists of 35 rules altogether. To classify the present driving condition vehicle acceleration is taken into consideration. This should be explained for three situations: ⦿ D v a Positive : Braking situation, all wheels are weighted low because of wheel slips appearing. ⦿ D v a Zero : If wheel speeds tend to constant driving the acceleration signal is low weighted in order to adjust the sensor. ⦿ D v a Negative : The experimental car was rearwheel driven therefore rear wheels are less weighted than front wheels.

23. Negative Z ero Positive -50 -40 -30 -20 -10 0 10 20 30 40 50 a x corrected

24. Here, three linguistic values are sufficient. The output of the estimation is derived as a weighted sum of the wheel measurement plus the integrated and corrected acceleration:

25. ⦿ The Fuzzy-Controller uses two input values: ⦿ The wheel slip S B : ⦿ Wheel acceleration α: ⦿ with wheel speed v Wheel and vehicle speed v Fuz , which is given by the Sensor.

26. ⦿ The input variables are transformed into fuzzy variables slip and d v wheel/dt by the fuzzification process. ⦿ Both variables use seven linguistic values, the slip variable is described by the terms ⦿ slip = { zero, very small, too small, smaller than optimum, optimum, too large, very large } ⦿ and the acceleration d v wheel/dt by ⦿ d v wheel/dt = { negative large, negative medium, negative small, negative few, zero, positive small, positive large }.

27. ⦿ The optimal breaking pressure results from the defuzzification of the linguistic variable pressure . ⦿ Finally a three-step controller determines the position of the magnetic valves, whether the pressure should be increased, hold firm or decreased.

28. Antilock-Braking System and Vehicle Speed Estimation using Fuzzy Logic by Ralf Klein (Paper presented on 1st Embedded Computing Conference, October 1996, Paris) On Track 2 program by Bosch conducted in November 2009.