Enhancing constant force tracking in uncertain contact surfaces: An admittance controller utilizing virtual delayed resonator

IF 4.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Gang Wang , Nanzhi Xie , Honglei Che , Qi Zhang
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引用次数: 0

Abstract

The lack of precise environmental stiffness information can significantly compromise the accuracy of force control during manipulator contact operations, leading to undesirable jitter effects at the end-effector. To address this issue, a novel admittance control method is proposed in this paper, which employs a virtual delayed resonator to enhance force tracking accuracy and suppress robot jitter. The Takagi–Sugeno (T–S) fuzzy model is developed to mitigate the impact of environmental stiffness errors, as observed by the Extended Kalman Filter (EKF), on system performance. Additionally, the H control strategy, based on Linear Matrix Inequality (LMI), is implemented to ensure system stability under external disturbances. Simulation results demonstrate the effectiveness of the proposed method in achieving consistent force tracking and suppressing chatter. Experimental outcomes further validate that, even when the workpiece stiffness is unknown, the proposed approach effectively reduces jitter at the manipulator’s end-effector while improving force tracking accuracy.
增强不确定接触面恒力跟踪:利用虚拟延迟谐振器的导纳控制器
缺乏精确的环境刚度信息会严重影响机械臂接触操作过程中力控制的准确性,导致末端执行器产生不良的抖动效应。为了解决这一问题,本文提出了一种新的导纳控制方法,该方法采用虚拟延迟谐振器来提高力跟踪精度并抑制机器人抖动。建立了Takagi-Sugeno (T-S)模糊模型,以减轻扩展卡尔曼滤波(EKF)观察到的环境刚度误差对系统性能的影响。此外,采用基于线性矩阵不等式(LMI)的H∞控制策略,保证系统在外界干扰下的稳定性。仿真结果证明了该方法在实现一致力跟踪和抑制颤振方面的有效性。实验结果进一步验证了在工件刚度未知的情况下,该方法有效地降低了机械手末端执行器的抖动,提高了力跟踪精度。
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
Robotics and Autonomous Systems
Robotics and Autonomous Systems 工程技术-机器人学
CiteScore
9.00
自引率
7.00%
发文量
164
审稿时长
4.5 months
期刊介绍: Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.
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