Fuzzy model reference control with adaptation of input fuzzy sets
An improved adaptation mechanism to fuzzy model reference learning control (FMRLC) will be introduced in this paper. The main idea of the presented approach consists in including the controller input fuzzy sets into the adaptation process. In comparison with other FMRLC modifications the proposed method can be started with smaller number of input membership functions resulting in better reference signal tracking. Performance of the proposed procedure is demonstrated on control of a nonlinear laboratory system.
fuzzy model reference learning control self-learning controllers intelligent control adaptive fuzzy control