Fuzzy learning control for anti-skid braking systems
- 24 August 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
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).Keywords
This publication has 6 references indexed in Scilit:
- Fuzzy model reference learning controlPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Discrete-time controller design for robust vehicle tractionIEEE Control Systems Magazine, 1990
- Fuzzy logic in control systems: fuzzy logic controller. IIEEE Transactions on Systems, Man, and Cybernetics, 1990
- A describing-function approach to antiskid designIEEE Transactions on Vehicular Technology, 1981
- A linguistic self-organizing process controllerAutomatica, 1979
- Adaptive Brake Control SystemProceedings of the Institution of Mechanical Engineers, 1972