Active Compliance Control Based on EKF Torque Fusion for Robot Manipulators

Abstract

To improve the accuracy of torque estimation and compliance control of the force sensorless, we propose a torque fusion method based on extended Kalman filter (EKF), both the data of motor current and the harmonic reducer torsional deformation are involved. First, a nonlinear EKF is designed based on the motor side dynamic model and joint deformation nonlinear model. The experiment shows that the method overcomes the interference caused by the environment and nonlinear system factors. Then, an active compliance controller is designed with a nested loop framework based on the fused torque. The inner loop computes the dynamic torque as feedforward to compensate for the system's dynamic uncertainty. The outer loop is admittance control to realize the manipulator's active compliance with the fused torque. Experiments on the robotic manipulator show that the proposed torque estimation scheme can reduce the root mean square error (RMSE) to 0.1278N.m and the max error of the joint estimated torque to 0.34N.m. In addition, the force tracking accuracy of the proposed compliant controller can reach 3.6 N, and can be extended to more redundant joints.