Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

IF 15.3 1区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-01-21 DOI:10.1109/JAS.2024.124773

Qinglai Wei;Shanshan Jiao;Qi Dong;Fei-Yue Wang

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引用次数: 0

Abstract

This paper highlights the utilization of parallel control and adaptive dynamic programming (ADP) for event-triggered robust parallel optimal consensus control (ETRPOC) of uncertain nonlinear continuous-time multiagent systems (MASs). First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian., allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique”s introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then., an event-triggered mechanism is adopted to save communication resources while ensuring the system”s stability. The coupled Hamilton- Jacobi (HJ) equation”s solution is approximated using a critic neural network (NN)., whose weights are updated in response to events. Furthermore., theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded (UUB). Finally., numerical simulations demonstrate the effectiveness of the developed ETRPOC method.

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多智能体系统的事件触发鲁棒并行最优共识控制

本文重点研究了不确定非线性连续多智能体系统事件触发鲁棒并行最优一致控制（ETRPOC）的并行控制和自适应动态规划的应用。首先，由一个虚拟控制变量和一个由耦合哈密顿量得到的特定辅助变量组成的并联控制系统。，可以将一般系统转换为仿射系统。令人感兴趣的是，并行控制技术的引入为消除干扰的负面影响提供了前所未有的前景。然后。采用事件触发机制，在保证系统稳定性的同时节省通信资源。利用临界神经网络（NN）逼近耦合Hamilton- Jacobi方程的解。，其权重会根据事件更新。此外。理论分析表明，权重估计误差是一致最终有界的（UUB）。最后。，数值仿真验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ieee-Caa Journal of Automatica Sinica Engineering-Control and Systems Engineering

CiteScore

23.50

自引率

11.00%

发文量

880

期刊介绍： The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control. Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.