Fuzzy learning control for anti-skid braking systems

Although antiskid braking systems (ABSs) are designed to optimize braking effectiveness while maintaining steerability, their performance often degrades for harsh road conditions (e.g., icy/snowy roads). The authors introduce the idea of using the fuzzy model reference learning control (FMRLC) technique for maintaining adequate performance even under such adverse road conditions. This controller utilizes a learning mechanism which observes the plant outputs and adjusts the rules in a direct fuzzy controller so that the overall system behaves like a reference model which characterizes the desired behavior. The performance of the FMRLC-based ABS is demonstrated by simulation for various road conditions (wet asphalt, icy) and `split road conditions´ (the condition where, e.g. emergency braking occurs and the road switches from wet to icy or vice versa)