未知扰动非线性多智能体系统的自适应事件触发滑模容错一致控制

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Chunsong Han, Kang Wang, Tengyu Ma, Guangtao Ran, Juntao Pan
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引用次数: 0

摘要

研究了基于事件触发策略的不确定二阶先导-跟随非线性多智能体系统的鲁棒滑模容错一致性问题。首先,利用执行器故障率和事件触发阈值信息构建自适应事件触发滑模容错控制器,以保证质量的鲁棒一致性,优化变条件下的性能,减少执行器退化和网络资源的过度利用;此外,还确定了滑模带的范围。其次,利用李雅普诺夫稳定性理论,给出了前导-跟随非线性质量自适应跟踪性能的充分条件。通过构造等价连接,给出了鲁棒一致性误差的上界。第三,计算了二阶leader-follow MASs中事件触发的最小执行时间。最后,仿真结果验证了该方法利用事件触发SMC实现容错一致性的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Event-Triggered Sliding-Mode Fault-Tolerant Consensus Control for Nonlinear Multi-Agent Systems With Unknown Perturbations

This paper researched the robust sliding mode fault-tolerant consensus problem for uncertain second-order leader-following nonlinear multiagent systems (MASs) based on event-triggered strategies. First, an adaptive event-triggered sliding mode fault-tolerant controller was constructed by using the information of actuator failure rate and event-triggered threshold to ensure the robust consensus of MASs, optimize the performance under variable conditions, and reduce actuator degradation and overutilization of network resources. Additionally, the range of the sliding mode band is determined. Second, by employing the Lyapunov stability theory, a sufficient condition for the performance of leader-following nonlinear MASs adaptive tracking was given. And through constructing an equivalent connection, an upper bound for the robust consensus error is also provided. Third, the minimum execution time for event triggering in second-order leader-following MASs is computed. Finally, the simulation results validate the effectiveness of the suggested method for achieving fault-tolerant consensus using event-triggered SMC.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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