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Volume 9 Issue 1
Jan.  2022

IEEE/CAA Journal of Automatica Sinica

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Guohuai Lin, Hongyi Li, Hui Ma, Deyin Yao and Renquan Lu, "Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 111-122, Jan. 2022. doi: 10.1109/JAS.2020.1003596
Citation: Guohuai Lin, Hongyi Li, Hui Ma, Deyin Yao and Renquan Lu, "Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 111-122, Jan. 2022. doi: 10.1109/JAS.2020.1003596

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

doi: 10.1109/JAS.2020.1003596
Funds:  This work was partially supported by the National Natural Science Foundation of China (62033003, 62003098), the Local Innovative and Research Teams Project of Guangdong Special Support Program (2019BT02X353), and the China Postdoctoral Science Foundation (2019M662813, 2020T130124)
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  • This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader’s input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader’s state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.

     

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    Highlights

    • In this paper, the leader of the nonlinear MASs is considered as non-autonomous and the leader’s control input is time-varying, which is provided by the human operator. In addition, we remove the restriction assumption that a subset of followers can access the leader’s control input
    • Different from most existing results, the controller designed in this paper achieves the leader-following consensus via adaptive coupling strengths for online adjustment. Furthermore, the considered nonlinear MASs are more general than the high-order Brunovsky form nonlinear systems
    • By using the relative information of neighboring nodes, the neighborhood observer is designed to estimate the unmeasurable states of nonlinear MASs, and the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is constructed to achieve the leader-following consensus of MASs

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