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Self-Triggered Consensus Control of Multiagent Systems From Data
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DOI：10.1109/TAC.2024.3351865.png)
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摘要
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This article considers self-triggered consensus control (STC) of unknown linear multiagent systems (MASs). Self-triggering mechanisms (STMs) are widely used in MASs, thanks to their advantages in avoiding continuous monitoring and saving computing and communication resources. However, existing results require the knowledge of system matrices, which are difficult to obtain in real-world settings. To address this challenge, we present a data-driven approach to designing STMs for unknown MASs building upon the model-based solutions. Our approach leverages a system lifting method, which allows us to derive a data-driven representation for the MAS. Subsequently, a data-driven STC scheme is designed, which combines a data-driven STM with a state feedback control law. We establish a data-based stability criterion for asymptotic consensus of the closed-loop MAS in terms of linear matrix inequalities, whose solution provides a matrix for the STM as well as a stabilizing controller gain. Numerical tests are conducted to validate the correctness of the proposed data-driven STC.
Keyword:
Linear matrix inequalities
Data models
Symmetric matrices
Stability criteria
Noise measurement
Multi-agent systems
Monitoring
Consensus control
data-driven control
distributed control
self-triggered control
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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