A Model Predictive Controller Using Multiple Linear Models for Continuous Stirred Tank Reactor (CSTR) and ITS Implementation Issue

Abstract

The linear model predictive control is popular since the past century and witnessed a steadily increasing attention from theoretical as well as practical point of view in the area of Nonlinear Model Predictive Control (NMPC). The practical interest is driven by the fact that today processes need to be operated under robust Performance specifications. At the same time more and more constraints, stemming for example from environmental and safety considerations, need to be satisfied. Often these demands can only be met when process nonlinearities and constraints are explicitly considered in the controller. Linear Model Predictive Control (LMPC) schemes which make use of linear dynamic model for prediction, limit their applicability to a narrow range of operation to system which exhibit nonlinear dynamics. Most of the existing model predictive control algorithms for nonlinear systems require the solution of a non-convex nonlinear optimization problem within the interval of one sample time. In this Paper, we presented non linear system as a family of local linear models. Nonlinear Model Predictive Controller which will use these local linear models for prediction has been developed. Comparison of this NMPC method and nonlinear PID controller method for controlling a nonlinear process is made. The effectiveness of the proposed control schemes have been demonstrated on a Continuous Stirred Tank Reactor (CSTR) process, which exhibits nonlinear dynamic. Finally issues about practically implementing this Nonlinear Model Predictive Controller using dedicated ASIC's are discussed