A novel adaptive three stages model predictive control based on fuzzy systems: Application in MIMO controlling of MED-TVC process |
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| Institution: | 1. Research Institute of Petroleum Industry (RIPI), Tehran, P.O. Box: 14665-1998, Iran;2. R&D Department, Bonian Daneshpajouhan Institute, Gholhak Junc., Tehran, Iran;1. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, PR China;2. School of Automation Engineering, University of Electronic Science and Technology of China, Sichuan 611731, PR China;1. School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China;2. School of Engineering, University of South Wales, Pontypridd, CF37 1DL, UK;3. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China;1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology), Ministry of Education, China;1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, China;2. Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education Beijing 100083, China;3. National Center for Mathematics and Interdisciplinary Sciences & Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China;4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;1. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China;2. School of Automation, Chongqing University, Chongqing 400044, China;3. Department of Mechanical Engineering, Universidad Politcnica Salesiana, Cuenca, Ecuador |
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| Abstract: | In the present study, a novel technique is suggested for the adaptive non-linear model predictive control based on the fuzzy approach in three stages. In the presented approach, in the first stage, the prediction and control horizons are obtained from a fuzzy system in each control step. Another fuzzy system is employed to determine the weight factors before the optimization stage of developing new controller. The proposed controller gives the parameters of the model predictive control (MPC) in each control step in order to improve the performance of nonlinear systems. The proposed control scheme is compared with the traditional MPC and Generic Model Control for controlling MED-TVC process. The performances of the three proposed controllers have been investigated in the absence and presence of disturbance in order to evaluate the stability and robustness of the proposed controllers. The results reveal that the novel adaptive controller based on fuzzy approach performs better than the two other controllers in set-point tracking and disturbance rejection with lower IAE criteria. In addition, the average computational time for the adaptive MPC exhibits a decline of 34% in comparison with the traditional MPC. |
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