Communication Dans Un Congrès Année : 2024

Global Consensus for Heterogeneous Saturated Multi-Agent Systems via Sampled-Data Control

Résumé

This paper addresses the global consensus problem for multi-input multi-output saturated systems within a sampled-data framework, aiming to advance global consensus, manage heterogeneous actuator saturation across components, and preserve distributed characteristics by using sampleddata feedback. We propose a distributed control algorithm that incorporates a redesigned saturation function, represented as decentralized dynamic saturation levels. These levels for each agent's dimensions are autonomously updated through an adaptive strategy, which mitigates heterogeneous saturation by carefully selecting constant and time-varying saturation parameters in it. Lyapunov analysis proves that global consensus can be achieved under the proposed control law, provided the sampling periods of all agents remain below a calculated threshold. An example is given to demonstrate the effectiveness of this approach.

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hal-04989471 , version 1 (13-03-2025)

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Juan Qian, Xiaoling Wang, Daniele Astolfi, Housheng Su, Guo-Ping Jiang. Global Consensus for Heterogeneous Saturated Multi-Agent Systems via Sampled-Data Control. 2024 IEEE 63rd Conference on Decision and Control (CDC), IEEE, Dec 2024, Milan, Italy. pp.4680-4685, ⟨10.1109/CDC56724.2024.10886353⟩. ⟨hal-04989471⟩
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