Disturbance observer based adaptive predefined-time sliding mode control for robot manipulators with uncertainties and disturbances

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Guofa Sun, Qingxi Liu, Fengyang Pan, Jiaxin Zheng
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引用次数: 0

Abstract

This article develops a predefined-time sliding mode control approach for systems with external disturbances and uncertainties through a nonlinear disturbance observer (DO). For addressing predefined-time stabilization problem of robotic manipulator system, a predefined-time sliding mode surface is proposed, ensuring system states converge to origin within a predefined-time once sliding mode surface is attained. Compared to conventional fixed-time and finite-time control strategies, a distinctive advantage of this scheme is that system settling time can be explicitly chosen in advance and independent of system states. To achieve predefined-time performance, a disturbance observer is introduced to generate the disturbance estimate, which can be incorporated into controller to counteract disturbance. To address the systems uncertainty, an adaptive law is employed to estimate the unknown upper boundary of system uncertainties. Finally, the effectiveness and performance of the proposed scheme are illustrated by simulation and experiment.

基于扰动观测器的自适应预定义时间滑模控制,用于具有不确定性和扰动的机器人机械手
本文通过非线性扰动观测器(DO)为具有外部扰动和不确定性的系统开发了一种预定义时间滑模控制方法。为解决机器人机械手系统的预定义时间稳定问题,本文提出了一个预定义时间滑动模态面,确保系统状态在滑动模态面达到后的预定义时间内收敛到原点。与传统的固定时间和有限时间控制策略相比,该方案的一个显著优点是系统稳定时间可以事先明确选择,且与系统状态无关。为实现预定时间性能,引入了扰动观测器来生成扰动估计值,并将其纳入控制器以抵消扰动。为解决系统不确定性问题,采用了自适应法则来估计未知的系统不确定性上边界。最后,通过模拟和实验说明了所提方案的有效性和性能。
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
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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