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For a class of nonlinear leader-following systems, this chapter investigates their fault-tolerant consensus control designing. In the systems, the input powers of the followers are unknown and larger than one. Furthermore, the followers also are subject to unmodeled dynamics and system fault. A distributed adaptive neural consensus control scheme is constructed. In sense of graph theory and Lyapunov stability theory, it is proven that the consensus is obtained with each synchronization error between each follower and leader converging asymptotically to a neighborhood of the origin. Finally, for the method designed in this chapter, its effectiveness is shown by simulation results.

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Adaptive Neural Distributed Fault-Tolerant Consensus with Unmodeled Dynamics and Unknown Higher Input Power

  • 章节

  • 发表日期：

    2025年

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  • Qikun Shen,
  • Dezhi Xu

摘要

For a class of nonlinear leader-following systems, this chapter investigates their fault-tolerant consensus control designing. In the systems, the input powers of the followers are unknown and larger than one. Furthermore, the followers also are subject to unmodeled dynamics and system fault. A distributed adaptive neural consensus control scheme is constructed. In sense of graph theory and Lyapunov stability theory, it is proven that the consensus is obtained with each synchronization error between each follower and leader converging asymptotically to a neighborhood of the origin. Finally, for the method designed in this chapter, its effectiveness is shown by simulation results.