Adaptive Tracking and Admittance Control Strategies for Force-Sensorless Physical Human–Robot Interaction

Abstract

In this paper, we present two adaptive control frameworks for physical human–robot interaction without direct force measurements. One enabling the robot to guide human motion using tracking control, and the other allowing it to follow human motion via admittance control. Both frameworks incorporate adaptive algorithms to estimate interaction forces, which are then integrated into controller designs to enhance performance and adaptability. In the first framework, the estimated forces are used to compensate for human-applied forces, enabling the robot to accurately follow a predefined trajectory. In contrast, the second control framework uses the estimated forces to adjust the robot's reference velocity, aligning its motion to human intentions. We employ Lyapunov's technique to demonstrate the stability and uniform ultimate bounds of the responses. Simulation and experimental results are presented to validate the proposed control algorithms. These results indicate that the approaches provide promising solutions for human–robot interaction with reduced cost and complexity.