Behavior learning based distributed tracking control for human-in-the-loop multi-agent systems |
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| Institution: | 1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;2. Peng Cheng Laboratory, Shenzhen 518000, China;3. School of Computer Science and Technology, Tiangong University, Tianjin 300387, China;1. Department of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran;2. Department of Electrical Engineering, Qatar University, Doha, Qatar;1. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Fars, Iran;2. Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Fars, Iran;1. Department of Control Science and Engineering, Tongji University, Shanghai 200092, China;2. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;3. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China |
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| Abstract: | In this study, the distributed tracking problem for human-in-the-loop multi-agent systems (HiTL MASs) has been investigated. First, we construct an HiTL MAS model with a non-autonomous leader which can receive the control signal from a human operator and generate the desired trajectory. The human control signal is assumed to be generated by a leader’s state feedback control law with an unknown gain matrix that represents the control behavior of the human operator. Then, we propose a fully distributed adaptive control method that enables all followers to simultaneously track the human-controlled leader and online learn the unknown human operator’s feedback gain matrix. Furthermore, the parameter estimation error is also discussed, and all followers will learn the true value of the human operator’s feedback gain matrix when the state of the leader satisfies the persistent excitation (PE) condition. Moreover, a novel distributed adaptive control law is developed for each follower to remove the PE condition by utilizing the concurrent learning (CL) technique. Finally, simulated examples demonstrating the effectiveness of the proposed methodologies are presented. |
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