Nonlinear Predictive Neural Control

Abstract

Predictive control is now widely used by industry and a large number of implementation algorithms, including generalised predictive control (Clarke et al., 1987), dynamic matrix control (Cutler and Ramaker, 1980), extended prediction self-adaptive control (Keyser and Cauwenberghe, 1985), predictive function control (Richalet et al., 1987), extended horizon adaptive control (Ydstie, 1984) and unified predictive control (Soeterboek et al., 1990), have appeared in the literature. Most predictive control algorithms are based on a linear model of the process. However, industrial processes usually contain complex nonlinearities and a linear model may be acceptable only when the process is operating around an equilibrium point. If the process is highly nonlinear, a nonlinear model will be necessary to describe the behaviour of the process.