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Data-driven distributed consensus control for unknown MIMO multi-agent systems with hybrid scheduling strategy
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AI
DOI：10.1016/j.jfranklin.2026.108846.png)
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Abstract
En 中文
This study investigates the consensus tracking problem of multi-input multi-output (MIMO) nonlinear multi-agent systems (MASs) under limited communication bandwidth. For agents with unknown dynamical models, a distributed model-free sliding mode control (MFSMC) method is designed to ensure the convergence of the MASs without requiring accurate models. Furthermore, by adopting a hybrid scheduling strategy, the data transmission among agents can be dynamically allocated. In contrast to traditional channel scheduling strategies, the hybrid strategy establishes a dynamic interaction between channel resources and scheduling rules, with the goal of reconciling limited communication resources with required system performance. Simulation studies validate the effectiveness of the proposed method.
Keywords:
Multi-agent systems
Consensus
Hybrid scheduling strategy
Data-driven sliding mode control
Journal
J
IF:
3.7
Papers:
6.3K
Citations:
1.5W