Kalman-Filter-Based Sensor Integration of Variable Power Assist Control Based on Human Stiffness Estimation

Abstract

In applications such as robot collaborating with human operators, the robot system must operate more slowly and be more compliant to safe user interaction. Moreover, a consideration of the dynamic properties of human operators is also important for the human application. According to such requirements, this paper presents a novel sensorless force control approach for the robot-assisted motion of the human arm. A twin direct-drive motor system with a wire rope has been developed to provide a precise force sensation and safety for human-robot interaction. In order to control the wire rope tension and human interaction force, two mode designs of the force control are realized. The common mode is utilized for the control of wire rope tension. In the differential mode, the Kalman-filter-based sensor integration for the interaction force observer is proposed in this paper. By combining two motor encoders and a commercial acceleration sensor together, white Gaussian noise is reduced, and high accurate feedback of the contact force is obtained. A variable power assist control method based on a real-time estimation of the stiffness of the human arm is also introduced. By considering the stiffness in human arm movements, this method increases the efficiency of the force control system and realizes comfortable force for human-robot interaction. The effectiveness of the method is verified by experimental results.