Antilock braking systems (ABS) have been developed to reduce tendency of wheel slip and to improve vehicle control during sudden braking especially on slippery road surfaces. The objective of such control is to increase wheel tractive force in the desired direction while maintaining adequate vehicle stability and steerability, passenger’s safety and comfort, and also reducing the vehicle stopping distance. Up to now, various control techniques have been developed for antilock braking systems. Some of these methods have not shown proper performance for different road conditions. In this thesis, a genetic-fuzzy controller and an optimized hybrid controller using a fuzzy system is proposed for antilock braking system. The objective function is defined to maintain wheel slip at a desired level, so that maximum wheel tractive force and maximum vehicle deceleration are obtained. All components of fuzzy system are optimized using a parallel genetic algorithm and error based optimization technique. The error based global optimization approach is used for fast convergence near optimum point. The input variables to the controller are obtained by wheel speed and vehicle acceleration sensors. All parameters of membership functions and rules of the fuzzy system that is Takagi-Sugeno-Kang (TSK) type, are optimized using a genetic algorithm and error based optimization technique. In order to obtain the optimum value in a shorter time and in a much wider region the error-based optimization approach is used. Performance of the proposed controller is tested on a vehicle model with the two types of hydraulic brake systems, for different road conditions. Simulation results, show very good performance of the controller as compared with other intelligent and conventional controllers proposed in technical references.
