Dual ML-ADHDP method for heterogeneous discrete-time nonlinear multi-agent systems with unknown dynamics and time delay |
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| Institution: | 1. School of Automation, China University of Geosciences, Wuhan 430074, China;2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China;3. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China;1. School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China;2. School of Aeronautics and Astronautics, Sichuan University, Chengdu 610207, China;3. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China;1. Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;2. Electronics Engineering Department, Universidad de Sevilla, Sevilla 41092, Spain;1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China;2. School of Science, Shandong Jianzhu University, Jinan, 250101, China;1. The Department of College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;2. The state Key Laboratory of Synthetical Automation for Process Industries, Shenyang, China |
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| Abstract: | This paper develops a new dual ML-ADHDP method to solve the optimal consensus problem (OCP) of a class of heterogeneous discrete-time nonlinear multi-agent systems (MASs) with unknown dynamics and time delay. A hierarchical and distributed control strategy is used to transform the original problem into nonlinear model reference adaptive control (MRAC) problems and an OCP of virtual linear MASs. For the nonlinear MRAC problems, a new multi-layer action-dependent heuristic dynamic programming (ML-ADHDP) method is developed to overcome the unknown dynamics and neural network estimation errors, which has higher control accuracy. In order to solve the OCP of virtual linear MASs and improve the convergence speed, a new multi-layer performance index is proposed. Then the ML-ADHDP method is used to solve the coupled Hamiltonian–Jacobi–Bellman equation and obtain the optimal virtual control. Theoretical analysis proves that the original MASs can achieve Nash equilibrium, and simulation results show that the developed dual ML-ADHDP method ensures better convergence speed and higher control accuracy of original MASs. |
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