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<p>This study focuses on the adaptive neural networks (NNs) prescribed-time control problem for a class of nonstrict-feedback nonlinear multi-agent systems (MASs) with unpredictable states via the self-triggered control (STC). In contrast with other observer-based adaptive fixed-time control strategy, we proposed a new control strategy based on a class of state observer with prescribed-time function to solve the problem of prescribed-time performance (PTP). In addition, the STC mechanism is able to promote system convergence more effectively with lower control cost. The proposed adaptive NNs controller enables all the followers to converge to a specific trajectory established by the leader within the prescribed-time. The effectiveness of this control strategy is confirmed by a simulation example.</p>

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Observer-based Self-triggered Adaptive Neural Network Control for Nonlinear Uncertain Multi-agent Systems With Prescribed-time

  • 期刊论文

  • 发表日期：

    2025年7月14日

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  • Jian Wu,
  • Xiangwen Wang,
  • Haofan Qiu,
  • Dewen Cao

摘要

This study focuses on the adaptive neural networks (NNs) prescribed-time control problem for a class of nonstrict-feedback nonlinear multi-agent systems (MASs) with unpredictable states via the self-triggered control (STC). In contrast with other observer-based adaptive fixed-time control strategy, we proposed a new control strategy based on a class of state observer with prescribed-time function to solve the problem of prescribed-time performance (PTP). In addition, the STC mechanism is able to promote system convergence more effectively with lower control cost. The proposed adaptive NNs controller enables all the followers to converge to a specific trajectory established by the leader within the prescribed-time. The effectiveness of this control strategy is confirmed by a simulation example.