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arXiv 2604.09323cs.RO
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在未知环境中具有鲁棒性的自适应反步阻抗控制

Robust Adaptive Backstepping Impedance Control of Robots in Unknown Environments

Research Center for Systems and Technologies (SYSTEC), ARISE, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal(系统与技术研究中心(SYSTEC),ARISE,工程学院,波尔图大学,葡萄牙4200-465波尔图);Intelligent Autonomous Systems Lab, Department of Computer Science, TU Darmstadt, Germany(智能自主系统实验室,计算机科学系,达姆施塔特技术大学,德国)

Reza Nazmara, Alap Kshirsagar, Jan Peters, A. Pedro Aguiar

AI总结:

本文提出了一种针对在接触丰富且不确定环境中操作的机器人鲁棒自适应反步阻抗控制(RABIC)策略,该策略考虑了系统的完整耦合动力学,并明确考虑了外部扰动和未建模动力学等关键不确定性来源,而无需机器人动态参数。通过反步方法设计内环以跟踪参考阻抗模型,利用泰勒级数估计器估计系统动力学并采用自适应估计器确定外部力的上界。稳定性分析证明了整体系统的半全局有限时间稳定性。通过模拟移动机械臂场景和对实际Franka Emika Panda机器人的实验评估,证明了所提方法在安全性、轨迹跟踪和力监测方面优于PD控制。

摘要:

本文提出了一种鲁棒自适应反步阻抗控制(RABIC)策略,用于在接触丰富和不确定环境中操作的机器人。所提出的控制策略考虑了系统的完整耦合动力学,并明确考虑了外部扰动和未建模动力学等关键不确定性来源,而无需机器人动态参数。我们提出了一种基于反步的自适应阻抗控制方案用于内环以跟踪参考阻抗模型。为了处理不确定性,我们采用基于泰勒级数的估计器来估计系统动力学,并采用自适应估计器来确定外部力的上界。稳定性分析证明了整体系统的半全局有限时间稳定性。为了证明所提方法的有效性,进行了模拟移动机械臂场景和对实际Franka Emika Panda机器人的真实实验评估。所提出的方法在安全性和轨迹跟踪及力监测方面优于PD控制。总体而言,RABIC框架为未来关于耦合移动和固定串联机械臂的自适应和学习阻抗控制的研究提供了坚实的基础。

英文摘要:

This paper presents a Robust Adaptive Backstepping Impedance Control (RABIC) strategy for robots operating in contact-rich and uncertain environments. The proposed control strategy considers the complete coupled dynamics of the system and explicitly accounts for key sources of uncertainty, including external disturbances and unmodeled dynamics, while not requiring the robot's dynamic parameters in implementation. We propose a backstepping-based adaptive impedance control scheme for the inner loop to track the reference impedance model. To handle uncertainties, we employ a Taylor series-based estimator for system dynamics and an adaptive estimator for determining the upper bound of external forces. Stability analysis demonstrates the semi-global practical finite-time stability of the overall system. To demonstrate the effectiveness of the proposed method, a simulated mobile manipulator scenario and experimental evaluations on a real Franka Emika Panda robot were conducted. The proposed approach exhibits safer performance compared to PD control while ensuring trajectory tracking and force monitoring. Overall, the RABIC framework provides a solid basis for future research on adaptive and learning-based impedance control for coupled mobile and fixed serially linked manipulators.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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