Nonlinear model predictive control with wiener model and laguerre function for CSTR process

Abstract

This paper presents a nonlinear model predictive control (NMPC) based on Wiener model and Laguerre function. Employing a Wiener model in NMPC can handle the nonlinearity in the controlled plant and retain all important properties of linear model predictive control (MPC) with a quadratic function. However, the number of variables varying with control horizon of the optimization problem can be very large, leading to a poorly numerical condition and heavy computational load. In this work, we use the Laguerre function to handle this problem. NMPC with the Lagurre function can reduce the number of variables used in the optimization problem. As a result, NMPC with the Lagurre function can be readily and efficiently solved. We demonstrate the effectiveness of the proposed method with an application to continuous stirred tank reactor (CSTR) process.