Distributed data-driven iterative learning control for multi-agent systems with unknown input-output coupled parameters

Duhui Chang; Yan Geng; Duhui Chang; Yan Geng

doi:10.3934/era.2025039

Electronic Research Archive

2025, Volume 33, Issue 2: 867-889. doi: 10.3934/era.2025039

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Research article

Distributed data-driven iterative learning control for multi-agent systems with unknown input-output coupled parameters

Duhui Chang ,
Yan Geng ^,

School of Science, Xi'an Polytechnic University, Xi'an 710048, China

Received: 17 October 2024 Revised: 11 January 2025 Accepted: 22 January 2025 Published: 14 February 2025

This article studies a distributed data-driven iterative learning control (ILC) strategy based on the identified input–output coupled parameters (IOCPs) to address the consensus trajectory tracking problem of discrete time-varying multi-agent systems (MASs). First, by leveraging the repeatability of the control system, a special learning scheme is designed by using system input and output data to identify the unknown IOCPs. Then the reciprocal of the identified IOCPs is selected as the learning gain to construct the ILC law of the MASs. Second, the case of measurement noise in the MASs is considered, where the maximum allowable control deviation is incorporated into the learning mechanism for identification of the IOCPs, thereby minimizing adverse effects of the noise on the learning scheme's performance and bolstering robustness. Finally, three numerical simulations are employed to validate the effectiveness of the designed IOCP identification method and iterative learning control strategy.
- iterative learning control,
- IOCPs,
- multi-agent systems,
- parameter identification
Citation: Duhui Chang, Yan Geng. Distributed data-driven iterative learning control for multi-agent systems with unknown input-output coupled parameters[J]. Electronic Research Archive, 2025, 33(2): 867-889. doi: 10.3934/era.2025039

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Abstract

This article studies a distributed data-driven iterative learning control (ILC) strategy based on the identified input–output coupled parameters (IOCPs) to address the consensus trajectory tracking problem of discrete time-varying multi-agent systems (MASs). First, by leveraging the repeatability of the control system, a special learning scheme is designed by using system input and output data to identify the unknown IOCPs. Then the reciprocal of the identified IOCPs is selected as the learning gain to construct the ILC law of the MASs. Second, the case of measurement noise in the MASs is considered, where the maximum allowable control deviation is incorporated into the learning mechanism for identification of the IOCPs, thereby minimizing adverse effects of the noise on the learning scheme's performance and bolstering robustness. Finally, three numerical simulations are employed to validate the effectiveness of the designed IOCP identification method and iterative learning control strategy.

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