Asynchronous control for nonlinear switched stochastic delayed systems with weighted mode-dependent average dwell time

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-08-05 DOI:10.1002/rnc.7579
Bo Liu, Longsuo Li, Yong Chen
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Abstract

This article investigates the asynchronous control for a class of nonlinear switched stochastic delayed systems. In response to this issue, the weighted mode-dependent average dwell time is first applied to switched stochastic systems as a more general switching signal. In addition, the asynchronous phenomenon is considered to address the mismatch in practical applications, where “mismatch” means the switching of the controllers has a delay to the switching of system modes. Then by selecting new multiple Lyapunov–Krasovskii functionals, sufficient conditions for mean-square exponential stability and an $$ {\mathscr{H}}_{\infty } $$ performance are derived. Finally, numerical examples are provided to illustrate the effectiveness of the proposed methods.

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具有加权模式相关平均停留时间的非线性开关随机延迟系统的异步控制
本文研究了一类非线性开关随机延迟系统的异步控制。针对这一问题,首先将与模式相关的加权平均停留时间作为一种更通用的开关信号应用于开关随机系统。此外,还考虑了异步现象,以解决实际应用中的不匹配问题,这里的 "不匹配 "是指控制器的切换与系统模式的切换存在延迟。然后,通过选择新的多重 Lyapunov-Krasovskii 函数,得出了均方指数稳定性和性能的充分条件。最后,通过数值示例说明了所提方法的有效性。
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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.
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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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