无需参数即可实现机器人在复杂环境中的稳定力控与轨迹跟踪。
Robust Adaptive Backstepping Impedance Control of Robots in Unknown Environments
- 基于反步法设计自适应阻抗控制，实时补偿未知干扰与未建模动态。
- 通过泰勒展开估计系统动态，自适应估算外部力上限，提升鲁棒性。
- 适用于真实机器人（如Franka Panda）的力控场景，适合工业协作任务。
本文提出一种针对接触密集且环境不确定的机器人系统的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，显式处理外部扰动和未建模动态等关键不确定性，且在实现中无需机器人动力学参数。内环采用基于反步法的自适应阻抗控制以跟踪参考阻抗模型；为应对不确定性，引入基于泰勒级数的系统动态估计器和自适应估计器以确定外部力的上界。稳定性分析表明整体系统具有半全局实用有限时间稳定性。通过模拟移动机械臂场景及在真实Franka Emika Panda机器人上的实验验证了所提方法的有效性。相比传统PD控制，RABIC展现出更优的安全性能，同时保证轨迹跟踪与力监测。总体而言，RABIC框架为未来耦合式移动与固定串联机械臂的自适应与学习型阻抗控制研究提供了坚实基础。
原文摘要 · Abstract (English)
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.
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