A Design of CMAC Based Intelligent PID Controllers

Abstract

Owing to the rapid progress of the computer technology in recent years, lots of works about intelligent control schemes have been developed in the field of control engineering. However, PID control algorithms still continue to be widely used for most industrial control systems, particularly in chemical process industries. However, it is difficult to find a suitable set of PID gains, because most processes are represented by nonlinear systems. On the other hand, in recent years, various neural networks (NNs) have been proposed. These technologies of the NNs enable us to deal with the nonlinear systems, and they play an important role in the field of control engineering. Furthermore, a Cerebellar Model Articulation Controller (CMAC) has been proposed as one of artificial neural networks. This paper presents a new design scheme of intelligent PID controllers whose PID gains are generated by using CMACs. Owing to the high-generalizing ability of the CMAC, the proposed scheme does not need large learning time with comparison to the conventional NN. Furthermore, the computational burden can be drastically reduced and the robust tracking can be realized for untrained reference signals. The newly proposed control scheme is numerically evaluated on some simulation examples.