MODEL-PREDICTIVE CONTROL-BASED ON WIENER MODELS

Wiener models, consisting of a linear dynamic element followed in seri es by a static nonlinear element, are considered to be ideal for repre senting a wide range of nonlinear process behavior. They are relativel y simple models requiring little more effort in development than a sta ndard linear model, yet offer superior characterization of systems wit h highly nonlinear gains. Wiener models may be incorporated into model predictive control (MPC) schemes in a unique way which effectively re moves the nonlinearity from the control problem, preserving many of th e favorable properties of linear MPC. This paper examines various mode l structures including ARX and step-response models with polynomial or spline nonlinearities and their corresponding identification strategi es. These techniques are then applied to an experimental pH neutraliza tion process where the performance of Wiener MPC is compared with that of the linear MPC and the benchmark PID control to showcase the salie nt features of this new approach. (C) 1997 Elsevier Science Ltd.