Adaptive Impedance and Admittance Controls for Physical Human-Robot Interaction with Force-Sensorless

Abstract

In this paper, we introduce control frameworks for physical human-robot interaction that rely on adaptive impedance learning and without force measurement. The adaptation laws are specifically designed to estimate human interaction forces, eliminating the need for a force sensor. These estimated forces are then utilized in the two controller designs. In the first one, estimated forces are used to compensate for the human's force, ensuring the robot tracks a predefined trajectory. Conversely, the second control law uses the estimated forces to adjust the robot's reference velocity in compliance with human intention. We employ Lyapunov's technique to demonstrate stability and the uniform ultimate boundedness of the responses. Simulation results are presented to validate the proposed control algorithms. These results indicate that the approaches offer promising solutions for human-robot interaction with reduced cost and complexity.