Design of Multi Model Predictive Control for nonlinear process plant

• Main Authors: Hung, N.T., Ismail, I., Saad, N.B., Ibrahim, R., Irfan, M.
• Format: Conference or Workshop Item
• Published: IEEE Computer Society 2014
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906335936&doi=10.1109%2fICIAS.2014.6869482&partnerID=40&md5=17e131a2e802672f01f71b47f92bb34b; http://eprints.utp.edu.my/32161/

This paper presents a new approach to deal with the nonlinearity of control system by using Multi Model Predictive Control (MPC) strategies. The idea of this research is using Fuzzy model to divide the nonlinear system into several sub linear systems which can be applied linear MPC controller. Firstly, the structure of Takagi-Sugeno (T-S) Fuzzy model is developed and optimized using Subtractive Clustering method. Then the obtained T-S Fuzzy model is trained using Adaptive-Network Based Fuzzy System (ANFIS) to derive optimal the parameters of models. Since the obtained T-S Fuzzy model is described in number of rules (local model) which present linear relationship between outputs and inputs so that a number of linear MPC controller is designed for each local model. The global control signal is combined from control signal of each local MPC controller by parallel distributed compensation technique. The proposed multi MPC scheme applying for CSTR nonlinear process shows that Multi Model Predictive Control based on T-S Fuzzy model can improve the performance of conventional MPC in nonlinear control system. © 2014 IEEE.