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Distributed model reference adaptive containment control of heterogeneous multi-agent systems with unknown uncertainties and directed topologies
Journal of the Franklin Institute ( IF 4.1 ) Pub Date : 2020-11-16 , DOI: 10.1016/j.jfranklin.2020.11.005
Dario Giuseppe Lui , Alberto Petrillo , Stefania Santini

In this paper, the containment control problem of heterogeneous uncertain high-order linear Multi-Agent Systems (MASs) is addressed and solved via a novel fully-Distributed Model Reference Adaptive Control (DMRAC) approach, where each follower computes its adaptive control action on the basis of local measurements, information shared with neighbors (within the communication range) and the matching errors w.r.t. its own reference model, without requiring any previous knowledge of the global directed communication topology structure. The approach inherits the robustness of the direct model reference adaptive control (MRAC) scheme and allows all agents converging towards the convex hull spanned by leaders while fulfilling at the same time local additional performance requirements at single-agent level, such as prescribed settling time, overshoot, etc. The asymptotic stability of the whole closed-loop network is analytically derived by exploiting the Lyapunov theory and the Barbalat lemma, hence proving that each follower converges to the convex hull spanned by the leaders, as well as the boundedness of the adaptive gains. Extensive numerical analysis for heterogeneous MAS composed of stable, unstable and oscillating agent dynamics are presented to validate the theoretical framework and to confirm the effectiveness of the proposed approach.



中文翻译:

具有未知不确定性和定向拓扑的异构多主体系统的分布式模型参考自适应包含控制

本文通过一种新颖的全分布式模型参考自适应控制(DMRAC)方法解决并解决了异构不确定性高阶线性多智能体系统(MAS)的密闭控制问题,其中每个跟随者均在其上计算其自适应控制作用。本地测量的基础,与邻居共享的信息(在通信范围内)以及匹配错误带有其自己的参考模型,而无需事先了解全局定向通信拓扑结构。该方法继承了直接模型参考自适应控制(MRAC)方案的鲁棒性,并允许所有代理收敛到领导者跨越的凸包,同时满足单代理级别的本地附加性能要求,例如规定的建立时间,超调等 通过利用李雅普诺夫理论和Barbalat引理分析得出整个闭环网络的渐近稳定性,因此证明了每个跟随者都收敛到领导者跨越的凸包以及自适应增益的有界性。提出了由稳定,不稳定和振动剂动力学组成的非均质MAS的广泛数值分析,以验证理论框架并确认所提出方法的有效性。

更新日期:2020-12-25
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