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基于钉扎的多智能体系统神经控制与自调节中间事件触发方法

Pinning-Based Neural Control for Multiagent Systems With Self-Regulation Intermediate Event-Triggered Method.

作者信息

Ren Hongru, Liu Zeyi, Liang Hongjing, Li Hongyi

出版信息

IEEE Trans Neural Netw Learn Syst. 2025 Apr;36(4):7252-7262. doi: 10.1109/TNNLS.2024.3386881. Epub 2025 Apr 4.

Abstract

A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step $n$ and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.

摘要

针对不确定非线性多智能体系统(MASs),提出了一种基于钉扎的自调节中间事件触发(ET)漏斗跟踪控制策略。基于反步法框架,设计了一种钉扎控制策略以实现跟踪控制目标,该策略仅使用智能体之间的通信权重,无需额外的反馈参数。此外,通过基于跟踪误差设计自调节触发条件,计算中间触发信号以取代控制器中的连续信号,从而实现控制器信号的非连续更新目标,并且该机制无需向控制器信号添加额外的补偿函数。同时,采用漏斗方法来限制第n步的误差,避免控制信号可能带来的负面影响。此外,通过干扰观测器对非线性非连续故障进行补偿。然后,利用李雅普诺夫稳定性定理证明闭环系统的所有信号都是半全局一致最终有界(SGUUB)的。最后,一些仿真结果证实了所提出控制方案的有效性。

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