Statistical information based two-layer model predictive control with dynamic economy and control performance for non-Gaussian stochastic process |
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| Authors: | Mifeng Ren Junghui Chen Peng Shi Gaowei Yan Lan Cheng |
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| Institution: | 1. College of Electrical Power and Engineering, Taiyuan University of Technology, 79 Yingze West Street, Taiyuan, China;2. Department of Chemical Engineering, Chung-Yuan Christian University, Taoyuan, Taiwan;3. School of Electrical and Electronic Engineering, University of Adelaide, Adealide, SA 5005, Australia;1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;3. School of Electrical Engineering and Automation, Qilu University of Technology, Shandong Academy of Sciences, Jinan 250353, China;4. Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China;2. Data Recovery Key Laboratory of Sichuan Province, Neijiang Normal University, Neijiang 641100, PR China;3. School of Mathematics Sciences, Sichuan Normal University, Chengdu 610066, China;4. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea;5. School of Information Science and Engineering, Chengdu University, Chengdu 610106, China;6. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, PR China;1. Information and Control Institute, Hangzhou Dianzi University, Hangzhou 310018, PR China;2. Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong |
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| Abstract: | In this paper, a two-layer model predictive control (MPC) hierarchical architecture of dynamic economic optimization (DEO) and reference tracking (RT) is proposed for non-Gaussian stochastic process in the framework of statistical information. In the upper layer, with state feedback and dynamic economic information, the economically optimal trajectories are estimated by entropy and mean based dynamic economic MPC, which uses the nonlinear dynamic model instead of the steady-state model. These estimated optimal trajectories from the upper layer are then employed as the reference trajectories of the lower layer control system. A survival information potential based MPC algorithm is used to maintain the controlled variables at their reference trajectories in the nonlinear system with non-Gaussian disturbances. The stability condition of closed-loop system dynamics is proved using the statistical linearization method. Finally, a numerical example and a continuous stirred-tank reactor are used to illustrate the merits of the proposed economic optimization and control method. |
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