Designing a genetic neural fuzzy antilock-brake-system controller

A typical antilock brake system (ABS) senses when the wheel lockup is to occur, releases the brakes momentarily, and then reapplies the brakes when the wheel spins up again. In this paper, a genetic neural fuzzy ABS controller is proposed that consists of a nonderivative neural optimizer and fuzzy-logic components (FLCs). The nonderivative optimizer finds the optimal wheel slips that maximize the road adhesion coefficient. The optimal wheel slips are for the front and rear wheels. The inputs to the FLC are the optimal wheel slips obtained by the nonderivative optimizer. The fuzzy components then compute brake torques that force the actual wheel slips to track the optimal wheel slips; these torques minimize the vehicle stopping distance. The FLCs are tuned using a genetic algorithm. The performance of the proposed controller is compared with the case when maximal brake torques are applied causing a wheel lockup, and with the case when wheel slips are kept constant while the road surface changes