Nonlinear predictive control of dynamic systems represented by Wiener–Hammerstein models

Abstract

This paper is concerned with computationally efficient nonlinear model predictive control (MPC) of dynamic systems described by cascade Wiener–Hammerstein models. The Wiener–Hammerstein structure consists of a nonlinear steady-state block sandwiched by two linear dynamic ones. Two nonlinear MPC algorithms are discussed in details. In the first case the model is successively linearised on-line for the current operating conditions, whereas in the second case the predicted output trajectory of the system is linearised along the trajectory of the future control scenario. Linearisation makes it possible to obtain quadratic optimisation MPC problems. In order to illustrate efficiency of the discussed nonlinear MPC algorithms, a heat exchanger represented by the Wiener–Hammerstein model is considered in simulations. The process is nonlinear, and a classical MPC strategy with linear process description does not lead to good control result. The discussed MPC algorithms with on-line linearisation are compared in terms of control quality and computational efficiency with the fully fledged nonlinear MPC approach with on-line nonlinear optimisation.