Dual-Condition event-Triggered distributed adaptive neural control for multi-Agent systems with nonlinear fault |
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| Institution: | 1. College of Control Science and Engineering, Bohai University, Jinzhou 121013, Liaoning, China;2. School of Mathematical Sciences, Bohai University, Jinzhou 121013, China;3. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;1. Department of Applied Mathematics, Bharathiar University, Coimbatore 641046, India;2. Department of Communications and Networks Engineering, Prince Sultan University, Saudi Arabia;1. College of Mathematics and Computer Science, Tongling University, Tongling, 244000, China;2. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China;1. Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. School of Automation, Beijing Institute of Technology, Beijing 100081, China;3. State Key Laboratory of IoTSC, University of Macau, Taipa, Macau;1. Department of Mathematics, Shanghai Maritime University, Shanghai 201306, China;2. College of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314001, China;3. School of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing 210096, China;4. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;1. National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing, 401331, PR China;2. Shanghai Institute of Applied Mathematics and Mechanics, Shanghai Key Laboratory of Mechanics in Energy Engineering, School of Mechanics and Engineering Science, Shanghai University, Shanghai, 200072, PR China;3. Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Australia;4. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, PR China;1. Department of Electrical Engineering, LI3CUB Laboratory, University of Biskra, Biskra, Algeria;2. Department of Electronics, Faculty of Technology, Contantine 1 University, Constantine, Algeria;3. Department of Electrical Engineering, LGEERE Laboratory, University of El Oued, El Oued, Algeria |
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| Abstract: | This paper studies the cooperative adaptive dual-condition event-triggered tracking control problem for the uncertain nonlinear nonstrict feedback multi-agent systems with nonlinear faults and unknown disturbances. Under the framework of backstepping technology, a new threshold update method is designed for the state event-triggered mechanism. At the same time, we develop a novel distributed dual-condition event-triggered strategy that combined the fixed threshold triggered mechanism acted on the controller with the new event-triggered mechanism, which can better reduce the waste of communication bandwidth. To deal with the algebraic loop problem caused by the non-affine nonlinear fault, the Butterworth low-pass filter is introduced. At the same time, the unknown function problems are solved by the neural network technology. All signals of the system are semiglobally uniformly ultimately bounded and the tracking performance is achieved, which proved by the Lyapunov stability theorem. Finally, the results of the simulation test the efficiency of the proposed control scheme. |
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