Improved control using extended non-minimal state space MPC and modified LQR for a kind of nonlinear systems |
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| Affiliation: | 1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;2. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China;3. Information Technology Center, Tsinghua University, Beijing 100084, China;4. The 61th Institute of Electronic System Engineering, Beijing 100039, China;1. Research Center on Technology of Information and Systems (CITIS) of the Autonomous University of Hidalgo State (UAEH), Mexico;2. Automatic Control Department of the Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Mexico;3. Electric and Electronic Engineering Department, Minatitlan Institute of Technology, Mexico;1. Department of Automation, Zhejiang University of Technology, Hangzhou 310023, PR China;2. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore;3. College of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, PR China |
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| Abstract: | Inspired by the state space model based predictive control, this paper presents the combination design of extended non-minimal state space predictive control (ENMSSPC) and modified linear quadratic regulator (LQR) for a kind of nonlinear process with output feedback coupling, which shows improved control performance for both model/plant match and model/plant mismatch cases. In many previous control methods for this kind of nonlinear systems, the nonlinear part is treated in different ways such as ignored, represented as a rough linear one or assumed to be time-variant when corresponding predictive control methods are designed. However, the above methods will generally lead to information loss, resulting in the influenced control performance. This paper will show that the ENMSSPC-LQ control structure will further improve closed-loop control performance concerning tracking ability and disturbance rejection compared with previous predictive control methods. |
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| Keywords: | State space predictive control Modified linear quadratic regulator Nonlinear systems |
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