Design of Optimized Nonlinear Controller for CSTR Process

Abstract

In this work, optimized Least Square Support Vector Machine (LSSVM) based Nonlinear Model Predictive Controller (NMPC) is proposed for the nonlinear Continuous Stirred Tank Reactor (CSTR) process. In NMPC, model of the dynamic system is needed to predict the controlled variable. The model of the nonlinear system is identified by either local linear models or through nonlinear model identification techniques viz., Neural Networks and LSSVM. The LSSVM overcomes the isssues involved in NN model such as overfitting and huge training set requirement issues. The regression performace of the LSSVM can be improved through the proper selection of its parameters viz., regularization parameter (C) and the kernel width parameter (σ). The Ant colony optimization (ACO) based Genetic Algorithm (GA) is proposed to tune the LSSVM parameters C and σ with different kernel function viz., linear, Radial Basis Function (RBF). To show the effectiveness of the proposed E-LSSVM scheme, it is presented to the CSTR process. Experimental evaluation under closed loop revealed Mean Square Error (MSE) value of 1.5210e-5 for 1500 prediction points with 100 training data under RBF kernel function which is better compares to previous approaches.