Robust Adaptive Backstepping Impedance Control of Robots in Unknown Environments

2026-04-10

AI Summary

This study addresses the challenge of simultaneously achieving high trajectory tracking accuracy, precise force control, and safe physical interaction for robots operating in contact-rich and uncertain environments. To this end, the authors propose a robust adaptive backstepping impedance control method that innovatively integrates backstepping design with adaptive impedance control. The approach employs an inner-loop controller to track a reference impedance model and leverages Taylor series expansion to estimate dynamic uncertainties, combined with an adaptive estimator for the upper bound of external forces. Notably, it requires no prior knowledge of system dynamics and uniformly handles coupled dynamics and unknown disturbances while guaranteeing semi-global practical finite-time stability. Simulations on both the Franka Emika Panda and a mobile manipulator demonstrate significant improvements over conventional PD control in trajectory tracking accuracy, force regulation, and interaction safety.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• impedance control
• uncertain environments
• robotic manipulation
• external disturbances
• unmodeled dynamics

Innovation

• Robust Adaptive Control
• Backstepping
• Impedance Control
• Uncertainty Estimation
• Finite-Time Stability