Observer-Based adaptive neural inverse optimal consensus control of nonlinear multiagent systems |
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| Institution: | 1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, Shandong, China;2. Faculty of Automation, Guangdong University of Technology, Guangzhou 510006, Guangdong, China;3. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, Guangdong, China;1. School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China;2. School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, 610106, China;3. School of Mathematics, Southeast University, Nanjing 210096, China;4. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;5. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, China;1. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China;2. Key Laboratory of Smart Manufacturing in Energy Chemical Process (East China University of Science and Technology), Ministry of Education, Shanghai 200237, China;1. Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing 211189, China;2. School of Science, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning 530006, China;3. NAAM Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia;4. Department of Mathematics, Quaid-I-Azam University, Islamabad, Pakistan;1. School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China;2. Research Center of Automation and Artificial Intelligence, Zhejiang University of Technology, Hangzhou, China;3. School of Mathematics, Southeast University, Nanjing 210096, China;4. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;5. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, 400067, China;6. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
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| Abstract: | The objective of this article is to present an adaptive neural inverse optimal consensus tracking control for nonlinear multi-agent systems (MASs) with unmeasurable states. In the control process, firstly, to approximate the unknown state, a new observer is created which includes the outputs of other agents and their estimated information. The neural network is used to reckon the uncertain nonlinear dynamic systems. Based on a new inverse optimal method and the construction of tuning functions, an adaptive neural inverse optimal consensus tracking controller is proposed, which does not depend on the auxiliary system, thus greatly reducing the computational load. The developed scheme not only insures that all signals of the system are cooperatively semiglobally uniformly ultimately bounded (CSUUB), but also realizes optimal control of all signals. Eventually, two simulations provide the effectiveness of the proposed scheme. |
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