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.

翻译：本文提出了一种面向接触丰富且不确定环境中的机器人鲁棒自适应反步阻抗控制（RABIC）策略。所提出的控制策略考虑了系统的完整耦合动力学，并显式处理了关键不确定性来源，包括外部扰动和未建模动力学，同时实现过程中无需机器人的动力学参数。我们提出了一种基于反步法的内环自适应阻抗控制方案，用于跟踪参考阻抗模型。为应对不确定性，我们采用基于泰勒级数的估计器处理系统动力学，并利用自适应估计器确定外力上界。稳定性分析表明，整体系统具有半全局实际有限时间稳定性。为验证该方法的有效性，开展了模拟移动机械手场景实验及真实Franka Emika Panda机器人上的实验评估。相较于PD控制，所提方法在保证轨迹跟踪与力监控的同时展现出更安全的性能。总体而言，RABIC框架为面向耦合移动与固定串联机械手的自适应及基于学习的阻抗控制研究奠定了坚实基础。