ABS System using Fuzzy Logic

Published on 19 May 2022. Written by Koustubh Prem

Car security system can be divided into two main parts: one is the active security systems. The other is the passive security system. Anti-lock braking system (ABS) is an important active security system. It can effectively enhance the driving stability, reduce the braking distance, and to some extent, prevent the accident from happening. The emergency brake of a moving car may cause sudden locking of the wheel.

The front wheel locking will cause loss of vehicle steering force, the rear wheels locking will make the vehicle slide sideways and tail flick. These problems are often one of the factors causing the accident. If a vehicle equipped with ABS, the tires will not be in a locking state when there is emergency braking, which enhances security. We will be using Matlab/ Simulink software to implement the simulation of the anti-lock braking system, as well as modeling and control of the ABS.

In order to prevent the wheel from locking when there is an emergency braking, the wheel speed sensors are used for the detection of wheel speed signal, ECU is calculated to determine whether the wheel can be locked, and then control the brake pressure regulator, pressure regulator will control of braking force by adjusting the wheel cylinder pressure, the slip ratio will remain at 20%-30% of the peak to get the best braking effect. In recent years, the research and development of the control strategy is based on the slip ratio, through gain scheduling PID control, variable structure control or fuzzy control method to keep the best slip ratio in the braking process.

Project Implementation:

Vehicle Dynamic Model: Considering the simplified design and the higher requirements of real-time control choose a single wheel model to design and build the simulation system. At the same time, the air resistance and the resistance of the wheel should be ignored which will be given in term of an equation. We will get a Wheel motion equation which will be having the factors like Ground supporting force, car tire radius, braking torque and speed of the car which are directly proportional to wheel moment of inertia.
Tire Model: Tire model is mainly to describe the relationship between adhesion coefficients and slip ratio. Road adhesion coefficient - slip ratio is a nonlinear relationship. In order to simplify the tire model, use two linear equations to approximate the road adhesion coefficient of a slip rate curve, which is called the Dugoff model.
Road Model: road data will be purely based on the type of road on which the testing is to be done so for that take 3-4 different types of roads like dry asphalt, wet road and ice road.
Once, all the data and models are ready we will dig into the Fuzzy logic controller design.
Fuzzy control has two inputs: the error of slip(error), and the error change ratio where the output is the brake pressure. In fuzzy logic, it represents the degree of truth as an extension of valuation. In fuzzy logic, it represents the degree of truth as an extension of valuation. The membership function of error requires a high sensitivity, so the choice is the triangle function. The sensitivity of the membership function of error-c is relatively small, so it is a combination of the trapezoidal and the Fermi curve.
The language input parameters will be negative, zero and Positive and output will be reducing the pressure, increase the pressure and hold the pressure. Now both the errors will be tested or compared with the input parameters and design set of control rules. Test the simulation on all the three road conditions.
Observe the wheel slip ratio of three kinds of road conditions, it can be found that when the coefficient of attachment is larger, the change of the slip ratio is small, and basically is the optimal slip ratio of 0.2. When the adhesion coefficient is small, the change of the slip rate is larger, and it is relatively unstable, sometimes it is far from the best slip rate.

Kit required to develop ABS System using Fuzzy Logic:

Technologies you will learn by working on ABS System using Fuzzy Logic:

ABS System using Fuzzy Logic

Project Implementation:

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