Adaptive state observer event-triggered consensus control for multi-agent systems with actuator failures

Kairui Chen; Yongping Du; Shuyan Xia; Kairui Chen; Yongping Du; Shuyan Xia

doi:10.3934/math.20241258

AIMS Mathematics

2024, Volume 9, Issue 9: 25752-25775. doi: 10.3934/math.20241258

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Research article

Adaptive state observer event-triggered consensus control for multi-agent systems with actuator failures

1.
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
2.
School of Computer & Information, Qiannan Normal University for Nationalities, Duyun 558000, China

Received: 27 June 2024 Revised: 23 August 2024 Accepted: 30 August 2024 Published: 04 September 2024
MSC : 93A16, 93C10

An adaptive neural network event-triggered consensus control method incorporating a state observer was proposed for a class of uncertain nonlinear multi-agent systems (MASs) with actuator failures. To begin, a state observer was constructed in an adaptive backstepping framework to estimate the MASs' unmeasurable states, and a radial basis function neural network (RBFNN) was employed to approximate the unknown nonlinear function of MASs. Meanwhile, to reduce the impact of actuator failure on the performance of MASs, the adaptive event-triggered mechanism (ETM) was designed to dynamically compensate for actuator failures, which alleviated the communication burden among individual agents by decreasing the update frequency of the control signals. Furthermore, all followers can track the leader's output signal with the synchronization errors converging to zero. Finally, simulation examples were used to verify the effectiveness of the proposed control strategy.
- multi-agent systems,
- consensus control,
- actuator failures,
- state observer design,
- event-triggered mechanism
Citation: Kairui Chen, Yongping Du, Shuyan Xia. Adaptive state observer event-triggered consensus control for multi-agent systems with actuator failures[J]. AIMS Mathematics, 2024, 9(9): 25752-25775. doi: 10.3934/math.20241258

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Abstract

An adaptive neural network event-triggered consensus control method incorporating a state observer was proposed for a class of uncertain nonlinear multi-agent systems (MASs) with actuator failures. To begin, a state observer was constructed in an adaptive backstepping framework to estimate the MASs' unmeasurable states, and a radial basis function neural network (RBFNN) was employed to approximate the unknown nonlinear function of MASs. Meanwhile, to reduce the impact of actuator failure on the performance of MASs, the adaptive event-triggered mechanism (ETM) was designed to dynamically compensate for actuator failures, which alleviated the communication burden among individual agents by decreasing the update frequency of the control signals. Furthermore, all followers can track the leader's output signal with the synchronization errors converging to zero. Finally, simulation examples were used to verify the effectiveness of the proposed control strategy.

References

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