Adaptive torque estimation of robot joint with harmonic drive transmission. (November 2017) - Shi, Zhiguo; Li, Yuankai; Liu, Guangjun
Highlights: A novel torque estimation method is proposed for robot joint. Adapt torque estimation filtering optimality and robustness to the load variation. The design of redundant factor of RARKF is a new technology. The impacts of time delay and random error have been analyzed. It can be applied to robot force and compliance, and collision detection. Abstract: Robot joint torque estimation using input and output position measurements is a promising technique, but the result may be affected by the load variation of the joint. In this paper, a torque estimation method with adaptive robustness and optimality adjustment according to load variation is proposed for robot joint with harmonic drive transmission. Based on a harmonic drive model and a redundant adaptive robust Kalman filter (RARKF), the proposed approach can adapt torque estimation filtering optimality and robustness to the load variation by self-tuning the filtering gain and self-switching the filtering mode between optimal and robust. The redundant factor of RARKF is designed as a function of the motor current for tolerating the modeling error and load-dependent filtering mode switching. The proposed joint torque estimation method has been experimentally studied in comparison with a commercial torque sensor and two representative filtering methods. The results have demonstrated the effectiveness of the proposed torque estimation technique.