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<p>In this paper, the distributed optimal consensus (DOC) problem for high-order nonlinear multi-agent systems (MASs) under unbalanced digraphs is investigated. First, we propose a new continuous-time DOC algorithm by utilizing parameter compensators, left eigenvector estimators, and similarity transformation, which ensures the asymptotic stability of high-order nonlinear MASs. Notably, this algorithm neither relies on additional reference signals nor assumes the strong convexity of the local cost function. Then, to further reduce communication overhead, the algorithm is extended to an event-triggered framework that integrates event-triggered controllers and estimators, effectively avoiding Zeno behavior while preserving convergence performance. Finally, the validity of the proposed approaches is demonstrated through two simulation examples.</p>

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Event-triggered distributed optimal consensus algorithm for high-order nonlinear agents under unbalanced digraphs

  • 期刊论文

  • 发表日期：

    2025年4月16日

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  • Wenqiang Wu,
  • Wenbo Zhu,
  • Qingling Wang

摘要

In this paper, the distributed optimal consensus (DOC) problem for high-order nonlinear multi-agent systems (MASs) under unbalanced digraphs is investigated. First, we propose a new continuous-time DOC algorithm by utilizing parameter compensators, left eigenvector estimators, and similarity transformation, which ensures the asymptotic stability of high-order nonlinear MASs. Notably, this algorithm neither relies on additional reference signals nor assumes the strong convexity of the local cost function. Then, to further reduce communication overhead, the algorithm is extended to an event-triggered framework that integrates event-triggered controllers and estimators, effectively avoiding Zeno behavior while preserving convergence performance. Finally, the validity of the proposed approaches is demonstrated through two simulation examples.