CMAC neural networks and PID concurrent control algorithm and its application to nonlinear system control

Cerebellar Model Articulation Controller (CMAC) is a kind of adaptive ANN based on local learning, which has the characters of fast convergence speed, strong generation ability, simple structure. While its advantages over conventional control techniques have been recognized in the literature, it has primarily been in system identification and pattern recognition, but not in control application. A novel adaptive learning algorithm and concurrent control based on CMAC and PID is proposed in this paper. The proposed algorithm and controller are applied to two-order nonlinear system. The experimental and simulated results show that performance of the CMAC based controller using the proposed learning algorithm is more stable and effective than that of the conventional controllers.