In this paper, a design method of neural-net based PID controllers is proposed for multivariable nonlinear systems with mutual interactions. The proposed method adopt both a static pre-compensator and some multi-layered neural networks. The former is used for roughly decoupling the controlled object, and the latter is used in order to improve decoupling and to linearize the approximately decoupled controlled object. Also the design scheme based on the relationship between PID law and the generalized minimum variance control (GMVC) law is adopted. The effectivenes of the proposed control scheme is evaluated on a simulation example.

Lots of self-tuning control schemes have been proposed for tuning the parameters of control systems. Among them, pole-assignment schemes have been widely used for tuning the parameters of control systems with unknown time delays. They are usually classified into two methods, the implicit and the explicit methods according to how to identify the parameters. The latter has an advantage to design a control scheme by taking account of the stability margin and control performance. However, it involves a considerably computational burden to solve a Diophantine equation. A simple scheme is proposed in this paper, which can construct a multivariable self-tuning pole-assignment control system, while taking account of the stability margin and control performance without solving a Diophantine equation.