CONTROL OF NONLINEAR PROCESS USING NEURAL NETWORK BASED MODEL PREDICTIVE CONTROL

This paper presents a Neural Network based Model Predictive Control (NNMPC) strategy to control nonlinear process. Multilayer Perceptron Neural Network (MLP) is chosen to represent a Nonlinear Auto Regressive with eXogenous signal (NARX) model of a nonlinear system. NARX dynamic model is based on feed-forward architecture and offers good approximation capabilities along with robustness and accuracy. Based on the identified neural model, a generalized predictive control (GPC) algorithm is implemented to control the composition in acontinuous stirred tank reactor (CSTR), whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. NNMPC is tuned by selecting few horizon parameters and weighting factor. The tracking performance of the NNMPC is tested using different amplitude function as a reference signal on CSTR application. Also the robustness and performance is tested in the presence of disturbance on random reference signal.