具有不确定领导者的非线性多代理系统的采样数据合作半全局稳健实用输出调节

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-07-20 DOI:10.1002/rnc.7542
Wei Liu, Kaiji Zeng, Yu Liu
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引用次数: 0

摘要

本文探讨了领导者系统未知的非线性多代理系统的采样数据合作鲁棒实际输出调节问题。在本研究中,跟随者系统的非线性不受线性增长条件的限制。在鲁棒控制技术和自适应控制技术的基础上,开发了一种基于分布式输出的采样数据控制协议,以处理外系统的不确定性,外系统可以属于任何已知的大紧凑集。我们首先对闭环系统进行坐标变换,得到所谓的增强系统,然后对该系统进行稳定性分析。结果证明,稳态跟踪误差在原点的任意给定小邻域内。最后,通过一个数值实例验证了所提控制器的性能。
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Sampled-data cooperative semi-global robust practical output regulation for nonlinear multi-agent systems with an uncertain leader

This article explores the problem of sampled-data cooperative robust practical output regulation for nonlinear multi-agent systems whose leader system is unknown. In this study, the nonlinearities of the follower systems are not constrained by the linear growth condition. Based on the robust control technology and the adaptive control technology, a distributed output-based sampled-data control protocol is developed to deal with the uncertainty of the exosystem which can belong to any large known compact set. We first perform the coordinate transformation of closed-loop system to obtain the so-called augmented system, and then perform the stability analysis for this system. As a result, it is proven that the steady-state tracking errors are in any small given neighborhood of the origin. Finally, the performance of the proposed controller is validated by a numerical example.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
期刊最新文献
Issue Information Disturbance observer based adaptive predefined-time sliding mode control for robot manipulators with uncertainties and disturbances Issue Information Issue Information A stabilizing reinforcement learning approach for sampled systems with partially unknown models
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