Finite-time control of discrete-time semi-Markov jump linear systems: A self-triggered MPC approach |
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| Institution: | 1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, China;2. Department of Automatic Control, Robotics and Fluid Technique, Faculty of Mechanical and Civil Engineering, University of Kragujevac, Kraljevo 36000, Serbia;1. Electronic Information Engineering Key Laboratory of Electronic Information of State Ethnic Affairs Commission, College of Electrical Engineering, Southwest Minzu University, Chengdu, Sichuan, 610041, China;2. School of Mathematical Science, University of Electronic Science and Technology of China, Chengdu 611731, China;1. HSE University, Moscow, Russian Federation;2. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA;3. CINVESTAV-IPN, Department of Control Automatic, UMI 3575 CINVESTAV-CNRS, Mexico City, Mexico;4. Institut für Theoretische Informatik, Mathematik und Operations Research, Universität der Bundeswehr München, München, Germany;1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China;2. Institute of Electric Vehicle Driving System and Safety Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China;1. School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, PR China;2. Department of Automatic Control, Robotics and Fluid Technique, Faculty of Mechanical and Civil Engineering, University of Kragujevac, Kraljevo 36000, Serbia |
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| Abstract: | In this paper, a self-triggered model predictive control (MPC) strategy is developed for discrete-time semi-Markov jump linear systems to achieve a desired finite-time performance. To obtain the multi-step predictive states when system mode jumping is subject to the semi-Markov chain, the concept of multi-step semi-Markov kernel is addressed. Meanwhile, a self-triggered scheme is formulated to predict sampling instants automatically and to reduce the computational burden of the on-line solving of MPC. Furthermore, the co-design of the self-triggered scheme and the MPC approach is adjusted to design the control input when keeping the state trajectories within a pre-specified bound over a given time interval. Finally, a numerical example and a population ecological system are introduced to evaluate the effectiveness of the proposed control. |
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