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<p>This paper investigates the problem of fault-tolerant fuzzy iterative learning control (ILC) for hybrid-order nonlinear multi-agent systems (MASs) subject to actuator faults. The considered MASs comprise a combination of first- and second-order agents, incorporating nonlinear unmodeled dynamics as well as actuator bias and gain faults. To address these challenges, a novel fault-tolerant fuzzy ILC scheme is proposed. Fuzzy logic systems (FLSs) are employed to approximate unknown nonlinear dynamics, while Nussbaum gain functions and an adaptive iterative learning strategy are utilized to compensate for unknown fuzzy weight parameters and actuator faults. A Lyapunov-like function is designed to rigorously analyze the convergence of tracking errors. Finally, simulation results are provided to validate the effectiveness of the proposed control approach.</p>

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Fault-tolerant fuzzy iterative learning control of hybrid first- and second-order nonlinear multi-agent systems with actuator faults

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Hejing Li,
  • Junbin Gong,
  • Qiongxiao Liu

摘要

This paper investigates the problem of fault-tolerant fuzzy iterative learning control (ILC) for hybrid-order nonlinear multi-agent systems (MASs) subject to actuator faults. The considered MASs comprise a combination of first- and second-order agents, incorporating nonlinear unmodeled dynamics as well as actuator bias and gain faults. To address these challenges, a novel fault-tolerant fuzzy ILC scheme is proposed. Fuzzy logic systems (FLSs) are employed to approximate unknown nonlinear dynamics, while Nussbaum gain functions and an adaptive iterative learning strategy are utilized to compensate for unknown fuzzy weight parameters and actuator faults. A Lyapunov-like function is designed to rigorously analyze the convergence of tracking errors. Finally, simulation results are provided to validate the effectiveness of the proposed control approach.