Fuzzy learning control for antiskid braking systems

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