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Self-Learning Control for Multi-Agent Consensus
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DOI：10.3390/appliedmath6030037.png)
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Abstract
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This paper addresses the consensus problem in multi-agent systems via a self-learning control scheme that directly reuses prior control information to accelerate transient coordination while maintaining robustness. I study agents with linear dynamics and external disturbances, and design a lightweight self-learning consensus control law for the distributed consensus domain, formulated as ui(t)=k1ui(t-tau)+k2si(t) with learning intensity k1 and learning interval tau. I provide a Lyapunov-based stability proof showing uniform ultimate boundedness of the consensus error under bounded disturbances. Compared to non-learning consensus laws, the proposed strategy achieves faster agreement with reduced long-term effort and retains simplicity suitable for resource-constrained multi-agent platforms, while also achieving decent performance against external disturbances. Simulations validate the improved transient speed and steady accuracy. The full-version-source code is open-sourced.
Keywords:
multi-agent systems
consensus control
self-learning control
robustness
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