Learning Observer and Performance Tuning-Based Robust Consensus Policy for Multiagent Systems
Learning Observer and Performance Tuning-Based Robust Consensus Policy for Multiagent Systems
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学习观察者和基于性能调优的多智能体系统的鲁棒共识策略
DOI：
10.1109/jsyst.2020.3047644
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发表时间：
2022-03
影响因子：
4.4
通讯作者：
Caisheng Wei
中科院分区：
文献类型：
--
作者：
Chengxi Zhang;Jin Wu;Choon Ki Ahn;Zhongyang Fei;Caisheng Wei
本文通过一种基于学习控制器的性能调整控制策略来解决多智能体系统的一致性控制问题。具体地说，提出了一种新的学习观测器，同时重建复合非线性项和系统状态。基于学习观测器的重构信息，提出了一种新的性能调整控制策略，以处理内部非线性项和外部干扰作用在系统上，同时提供预定的共识性能。所提出的学习观测器在保证一致最终有界估计的同时，节省了计算资源，有利于多智能体系统。所提出的控制策略，结合观测器和性能调整，确保非理想摄动的鲁棒性和高精度的控制性能的同时。数学仿真验证了控制算法的有效性。
This article addresses the multiagent systems consensus control problem via a learning observer-based performance tuning control policy. Specifically, a novel learning observer is presented to reconstruct the compound nonlinear terms and system states simultaneously. Based on the learning observer’s reconstructed information, a novel performance tuning control policy is proposed to deal with the internal nonlinear terms and external disturbances acting on the system while providing prescribed consensus performance. The proposed learning observer can guarantee the uniformly ultimately bounded estimation while saving computing resources, which is beneficial to the multiagent system. The proposed control policy, combined with observer and performance tuning, ensures the robustness to nonideal perturbations and the high accuracy control performance simultaneously. Mathematical simulations verify the effectiveness of the control algorithm.
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