Identification and control of generalized hammerstein processes

Abstract

The Hammerstein model is a block-oriented nonlinear model, which consists of the cascade structure of a static nonlinear function followed by the linear dynamics. The generalized Hammerstein model is identified by updating the parameters of time-varying linear part and nonlinear part in an iterative manner. This method is applied to the identification of both SISO and MIMO generalized Hammerstein models. Simulation results demonstrate that generalized Hammerstein model has better predictive performance than the conventional Hammerstein model. Adaptive controller design methods for nonlinear processes using generalized Hammerstein model are proposed. For SISO processes, adaptive IMC design and adaptive PID controller are developed, while an adaptive decentralized PID controller is devised for MIMO processes. Simulation results show that the proposed adaptive controllers give better performance than their conventional counterparts.