Application of Fuzzy Logic in Antilock Braking System - seminar ppt

ANTILOCK BRAKING SYSTEM

An anti-lock braking system (ABS) is a safety system that prevents the wheels on a motor vehicle from locking up (or ceasing to rotate) while braking.

•A rotating road wheel allows the driver to maintain steering control under heavy braking by preventing a skid and allowing the wheel to continue interacting tractively with the road surface as directed by driver steering inputs. •ABS offers improved vehicle control and decreases stopping distances on dry and especially slippery surfaces for many drivers, but on loose surfaces like gravel and snow-on-pavement it can slightly increase braking distance, while still improving vehicle control. •Since initial widespread use in production cars, anti-lock braking systems have evolved considerably.

•Recent versions not only prevent wheel lock under braking, but also electronically control the front-to-rear brake bias.

How does an ABS work? •The anti-lock brake controller is also known as the CAB (Controller Anti-lock Brake). ABS Components There are four main components to an ABS system: Speed sensors Valves Pump Controller

Speed Sensors The anti-lock braking system needs some way of knowing when a wheel is about to lock up. The speed sensors, which are located at each wheel, or in some cases in the differential, provide this information. Valves There is a valve in the brake line of each brake controlled by the ABS. On some systems, the valve has three positions: In position one, the valve is open; pressure from the master cylinder is passed right through to the brake.

In position two, the valve blocks the line, isolating that brake from the master cylinder. This prevents the pressure from rising further should the driver push the brake pedal harder. In position three, the valve releases some of the pressure from the brake.

Pump Since the valve is able to release pressure from the brakes, there has to be some way to put that pressure back. That is what the pump does; when a valve reduces the pressure in a line, the pump is there to get the pressure back up.

Controller The controller is an ECU type unit in the car which receives information from each individual wheel speed sensor, in turn if a wheel looses traction the signal is sent to the controller, the controller will then limit the brakeforce and activate the ABS modulator which actuates the braking valves on and off.

•The ECU constantly monitors the rotational speed of each wheel. •When it detects a wheel rotating significantly slower than the others — a condition indicative of impending wheel lock — it actuates the valves to reduce hydraulic pressure to the brake at the affected wheel, thus reducing the braking force on that wheel. •The wheel then turns faster; when the ECU detects it is turning significantly faster than the others, brake hydraulic pressure to the wheel is increased so the braking force is reapplied and the wheel slows. • This process is repeated continuously, and can be detected by the driver via brake pedal pulsation. •Some anti-lock system can apply and release braking pressure 16 times per second. •The ECU is programmed to disregard differences in wheel rotative speed below a critical threshold, because when the car is turning, the two wheels towards the center of the curve turn slower than the outer two. For this same reason, a differential is used in virtually all roadgoing vehicles.

Fuzzy applied in ABS

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Vehicle dynamics and braking systems are complex and behave strongly non-linear which causes difficulties in developing a classical controller for ABS.

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Fuzzy logic, however facilitates such system designs and improves turning abilities.

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The underlying control philosophy takes into consideration wheel acceleration as well as wheel slip in order to recognize blocking tendencies.

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The knowledge of the actual vehicle velocity is necessary to calculate wheel slips.

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This is done by means of a good sensor, which weighs the inputs of a longitudinal acceleration sensor and four wheel speed sensors.

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If lockup tendency is detected, magnetic valves are switched to reduce brake pressure.

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Performance evaluation is based both on computer simulations and an experimental car.

Wheel model FZ: R: w: v: FL:

Wheel load Wheel radius Angular wheel frequency Velocity of wheel center Longitudinal force

Figure 1

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Calculating the wheel slip by

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the longitudinal wheel force results in

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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.

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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.

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Therefore a fast and accurate control system is required to keep wheel slips within the shaded area shown in Figure 1.

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Furthermore Figure 2 depicts the hydraulic unit including main brake cylinder, hydraulic lines and wheel brake cylinders.

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By means of two magnetic two-way valves each wheel, braking pressure pi, j is modulated.

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Three discrete conditions are possible: decrease pressure, hold pressure firm and increase pressure (up to main brake pressure level only).

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Each valve is hydraulically connected to the main brake cylinder, to the wheel brake cylinders and to the recirculation.

CG:

Center of gravitiy

ax:

Longitudinal acceleration

w i,j:

Angular wheel frequency

HU:

Hydraulic Unit

pi,j:

Wheel brake pressure

i:

l=left, r=right

j:

f=front, r=rear

Figure 2

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The knowledge of the actual vehicle speed over ground is vital in order to calculate wheel slips correctly.

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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.

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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 ax.

Figure 3

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In the data pre-processing block the measured signals are filtered by a lowpass and the inputs for the fuzzy estimator are calculated.

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Four wheels slip , and an acceleration value D va are calculated. The applied formulas are:

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whereby aOffset 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.

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v Fuz(k-1) is the estimated velocity of the previous cycle.

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A time-delay of T is expressed by the term 1/z.

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The fuzzy estimator itself is divided into two parts.

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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.

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This cascade structure is chosen to reduce the number of rules.

Figure 4

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

Figure 5

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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:

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D va Positive: Braking situation, all wheels are weighted low because of wheel slips appearing.

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D va Zero: If wheel speeds tend to constant driving the acceleration signal is low weighted in order to adjust the sensor.

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D va Negative: The experimental car was rearwheel driven therefore rear wheels are less weighted than front wheels.

Negative

Zero

Positive

-50 -40 -30 -20 -10 0 10 20 30 40 50

ax corrected

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:

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The Fuzzy-Controller uses two input values:

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The wheel slip SB:

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Wheel acceleration α:

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with wheel speed vWheel and vehicle speed vFuz, which is given by the Sensor.

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The input variables are transformed into fuzzy variables slip and dvwheel/dt by the fuzzification process.

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Both variables use seven linguistic values, the slip variable is described by the terms

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slip = {zero, very small, too small, smaller than optimum, optimum, too large, very large}

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and the acceleration dvwheel/dt by

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dvwheel/dt = {negative large, negative medium, negative small, negative few, zero, positive small, positive large}.

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As a result of two fuzzy variables, each of them having 7 labels, 49 different conditions are possible.

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The rule base is complete that means, all 49 rules are formulated and all 49 conditions are allowed. These rules create a nonlinear characteristic surface as shown in Figure 3.

Figure 3

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Using this characteristic surface, the two fuzzy input values slip and dvwheel/dt can be mapped to the fuzzy output value pressure. The labels for this value are:

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pressure = {positive fast, positive slow, zero, negative slow, negative fast}

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The optimal breaking pressure results from the defuzzification of

the linguistic variable pressure.

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Finally a three-step controller determines the position of the magnetic valves, whether the pressure should be increased, hold

firm or decreased.

1 Antilock-Braking System and Vehicle Speed Estimation using Fuzzy Logic by Ralf Klein (Paper presented on 1st Embedded Computing Conference, October 1996, Paris)

2 On Track 2 program by Bosch conducted in November 2009.