Stable Variable Impedance Control Via CLF-MPC for Physical Human-Robot Interaction

초록

Variable impedance control is a control strategy widely used in physical human-robot collaboration (pHRC) and physical human-robot interaction (pHRI). Variable stiffness and damping parameters improve adaptability to changing environments and enhance safety in human-robot interaction. However, these adaptive parameters can compromise the stability of the system without proper management, particularly in dynamic environments. To address this, we propose a real-time parameter prediction method for variable impedance control using model predictive control (MPC) with Control Lyapunov Function (CLF). Unlike the method that sets the terminal constraint as the equilibrium position, the proposed method guarantees system stability even when parameters change or external disturbances occur, ensuring safe and adaptive robot behavior. Moreover, the infeasibility issue is resolved by applying CLF instead of relying on the equilibrium position. Furthermore, considering stability throughout the prediction horizon, the stability of the system is strictly guaranteed. The proposed method was validated through comparative experiments with the method that sets the terminal constraint as the equilibrium position in both simulations and real-world environments using the Franka Emika Panda robot. Through these experiments, the proposed controller demonstrated a significant reduction in parameter computation time, achieving approximately 97.13% and 96.20% faster computation in simulation tests compared to conventional method, while consistently ensuring stability under various disturbances including human interaction, tool vibration, and contact loss scenarios. © 2025 IEEE.