在匹配和不匹配不确定性条件下,基于 RMRAC 的自适应西格码超扭曲滑动模式的 Lyapunov 稳定性分析

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-08-22 DOI:10.1002/rnc.7598
Guilherme Vieira Hollweg, Paulo Jefferson Dias de Oliveira Evald, Hilton Abílio Grundling, Wencong Su
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引用次数: 0

摘要

本文介绍了一种离散时间鲁棒模型参考自适应控制器和自适应西格码超扭曲滑动模式(RMRAC-ASSTSM),并利用李亚普诺夫稳定性理论对其进行了稳定性分析。这种控制结构对匹配和非匹配动态都具有鲁棒性。此外,由于使用了西格米函数的自适应超扭曲滑动模式作用,颤振现象在稳态下趋于抑制。仿真结果证实了所提出的控制结构的性能,考虑到一个二阶非最小相位不稳定工厂,可以看到当工厂呈现非建模动态时,RMRAC-ASSTSM 调节误差会收敛到一个有限的残差集。此外,还对基于 RMRAC 的类似控制结构进行了比较,该控制结构采用了带有符号函数的自适应超扭曲滑动模式。
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Lyapunov stability analysis of an RMRAC‐based adaptive sigmoid super‐twisting sliding mode under matched and unmatched uncertainties
This article presents a discrete‐time robust model reference adaptive controller and adaptive sigmoid super‐twisting sliding mode (RMRAC‐ASSTSM) and its stability analysis using Lyapunov stability theory. This control structure is robust to matched and unmatched dynamics. In addition, the chattering phenomenon tends to be suppressed in the steady state due to adaptive super‐twisting sliding mode action using a sigmoid function. The performance of the proposed control structure is corroborated with simulation results, considering a second‐order non‐minimum phase unstable plant, where it can be seen that RMRAC‐ASSTSM regulation errors converge to a finite residual set when the plant presents unmodeled dynamics. A comparison is also presented with a similar RMRAC‐based control structure with an adaptive super‐twisting sliding mode implemented with a sign function.
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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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