Distributed Constrained Optimization Algorithm for Higher-Order Multi-Agent Systems

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-07-18 DOI:10.1109/TSIPN.2024.3430492

Xiasheng Shi;Lingfei Su;Qing Wang

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Abstract

The distributed nonsmooth constrained optimization problems over higher-order systems are investigated in this study. The challenges lies in the fact that the output of the agent is directly controlled by the state variable rather than the control input. Compared to existing works, the local objective function is merely assumed to be nonsmooth. Firstly, an initialization-free fully distributed derivative feedback control scheme is developed for the known objective function over double-integrator systems. The local generic constraint is addressed by an adaptive nonnegative penalty factor. Secondly, an initialization-free fully distributed state feedback control scheme is proposed for the unknown objective function over double-integrator systems. Addressing the local box constraint involves incorporating an adaptive penalty factor. Thirdly, the above two algorithms are extended to the general higher-order systems using the tracking control method. In addition, the above-developed methods are proved to be asymptotically convergent under certain conditions. Eventually, the efficiency of the above-produced methods is shown via four simulation cases.

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高阶多代理系统的分布式约束优化算法

本研究探讨了高阶系统的分布式非光滑约束优化问题。所面临的挑战在于，代理的输出直接受状态变量而非控制输入的控制。与现有研究相比，本研究仅假设局部目标函数为非光滑。首先，针对双积分器系统的已知目标函数，开发了一种无初始化的全分布式导数反馈控制方案。局部通用约束通过自适应非负惩罚因子来解决。其次，针对双积分器系统的未知目标函数，提出了一种无初始化全分布式状态反馈控制方案。解决局部箱体约束的方法是加入自适应惩罚因子。第三，利用跟踪控制方法将上述两种算法扩展到一般的高阶系统。此外，还证明了上述方法在某些条件下具有渐进收敛性。最后，通过四个模拟案例证明了上述方法的效率。

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 Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.