二阶多代理系统的最优分布式时变编队控制:基于 LQR 的方法

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

摘要

研究了二阶多代理系统(MAS)的最优无领导和领导跟随时变编队(TVF)控制问题,提出了两种最优TVF控制协议,以实现所需的编队,并最小化包含合作性能指标和控制能量指标的综合优化函数。对于无领队情况,利用状态空间分解法将最优编队控制问题重新表述为无限时状态调节器问题,该问题受到能量和性能指标的特定约束,随后提出了最优 TVF 可实现性的解析准则。然后,将最优无领跑者 TVF 控制的结果扩展到具有切换拓扑的领跑者-跟随情况,主要挑战变为寻找最优控制器而非最优增益矩阵,并准确确定了综合指标的最优值。最后,提出了两个仿真案例来验证理论结果的有效性,并与前人的研究成果进行了比较,阐述了所提编队控制方法的最优性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal distributed time-varying formation control for second-order multiagent systems: LQR-based method

The optimal leaderless and leader-following time-varying formation (TVF) control problems for second-order multiagent systems (MASs) are investigated, where two optimal TVF control protocols are proposed to achieve the desired formations as well as minimize the comprehensive optimization function that contain the cooperative performance index and the control energy index. For leaderless case, the optimal formation control problem is reformulated as an infinite-time state regulator problem by employing the state space decomposition method, which is subject to specified constraints on energy and performance indices, and the analytic criterion for optimal TVF achievability is subsequently proposed. Then, the results of optimal leaderless TVF control are extended to the leader-following case with switching topologies, where the main challenge is changed to find the optimal controller rather than the optimal gain matrix, and the optimal value of the comprehensive index is accurately determined. Finally, two simulation cases are proposed to validate the effectiveness of the theoretical results, and comparisons with previous works are presented to expound the optimality of the proposed formation control method.

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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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