Model Predictive Control of a Highly Nonlinear Process Based on Piecewise Linear Wiener Models

Abstract

In this paper a nonlinear model predictive control (NMPC) based on a piecewise linear Wiener model is presented. The nonlinear gain of this particular Wiener model is approximated using the piecewise linear functions. This approach retains all the interested properties of the classical linear model predictive control (MPC) and keeps computations easy to solve due to the canonical structure of the nonlinear gain. The presented control scheme is applied to a pH neutralization process and simulation results are compared to linear model predictive control. Simulation results show that the nonlinear controller has better performance without any overshoot in comparison with linear MPC and also less steady-state error in tracking the set-points