Fixed-Time Sliding Mode Adaptive Control of Hydraulic Manipulator With Shutoff Deadzone and Uncertain Nonlinearity

IF 8.7 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Qing Guo;Haoran Zhan;Zhao Wang;Tieshan Li
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引用次数: 0

Abstract

There exist general model uncertainty and shutoff deadzone in hydraulic-driving plant due to unknown model parameters of mechanical structure and physical feature of electrohydraulic actuator, which will degrade the motion performance and stability. In this study, a fixed-time sliding mode adaptive control is presented in 2-DOF hydraulic manipulator to address these issues. First, the dynamic manipulator with two degree-of-freedom joint driving by hydraulic servo valve is setup via Lagrangian method. Then, a sliding mode disturbance observer is designed to approximate uncertain nonlinearity to ensure the lumped uncertainties convergence in a practical finite time. Furthermore, a parametric adaptive estimation is used to estimate unknown shutoff deadzone parameters. According to backstepping technique, a fixed-time convergence control is adopted to ensure all the system state errors converge into a zero neighborhood in a constant time not related to any initial condition. Finally, the fixed-time sliding mode adaptive controller has been verified in an experimental bench of 2-DOF manipulator.
具有关闭死区和不确定非线性的液压机械臂的定时滑模自适应控制
由于电液作动器机械结构和物理特性的模型参数未知,在液压传动装置中存在普遍的模型不确定性和关闭死区,从而降低了电液作动器的运动性能和稳定性。针对上述问题,提出了一种二自由度液压机械臂的定时滑模自适应控制方法。首先,利用拉格朗日方法建立了由液压伺服阀驱动的二自由度关节动态机械手;然后,设计了一个滑模扰动观测器来逼近不确定非线性,以保证集总不确定性在有限时间内收敛。在此基础上,采用参数自适应估计方法对未知截止死区参数进行估计。根据反演技术,采用固定时间收敛控制,保证系统所有状态误差在不与任何初始条件相关的恒定时间内收敛到零邻域。最后,在二自由度机械臂实验台上对所设计的定时滑模自适应控制器进行了验证。
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来源期刊
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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