A T-S fuzzy state observer-based model predictive reset control for a class of fuzzy nonlinear systems with event-triggered mechanism |
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| Institution: | 1. Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing, China;2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;3. Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, U.K.;1. Department of Automation, Chongqing University, Chongqing, 400044, China;2. Department of Automation, Tsinghua University, Beijing, 100084, China;1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China;2. Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada;3. College of Mathematical Sciences, Bohai University, Jinzhou 121000, China;1. School of Automobile, Chang''an University, Xi''an, 710064, China;2. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA |
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| Abstract: | In this study, the problem of observer-based control for a class of nonlinear systems using Takagi-Sugeno (T-S) fuzzy models is investigated. The observer-based model predictive event-triggered fuzzy reset controller is constructed by a T-S fuzzy state observer, an event-triggered fuzzy reset controller, and a model predictive mechanism. First, the proposed controller utilizes the T-S fuzzy model and is constructed based on state observations and discrete sampling output, which can greatly reduce the occupation of communication resources. Then, the model predictive strategy for reset law design is designed in this paper. With a reasonable reset of the controller state at certain instants, the performance of the reset control systems is improved. Finally, the validity of the proposed method is illustrated by simulation. The merits of the proposed controller in improving transient performance and reducing the communication occupation are demonstrated by comparing its results with the output feedback fuzzy controller and the first-order fuzzy reset controller. |
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