4.6 Article

Active Compliance Control Based on EKF Torque Fusion for Robot Manipulators

期刊

IEEE ROBOTICS AND AUTOMATION LETTERS
卷 8, 期 5, 页码 2668-2675

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2023.3258697

关键词

Torque; Estimation; Harmonic analysis; Deformation; Force; Robot sensing systems; Manipulators; Industrial robots; force control; force sensorless

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To improve the accuracy of torque estimation and compliance control without force sensors, a torque fusion method based on extended Kalman filter (EKF) is proposed, considering both motor current data and harmonic reducer torsional deformation. Firstly, a nonlinear EKF is designed based on the motor side dynamic model and joint deformation nonlinear model. Experimental results show that the method overcomes interference from the environment and nonlinear system factors. Then, an active compliance controller with a nested loop framework is designed based on the fused torque. The proposed torque estimation scheme reduces the root mean square error (RMSE) to 0.1278N·m and the maximum error of joint estimated torque to 0.34N·m, while the force tracking accuracy of the compliant controller reaches 3.6N and can be extended to more redundant joints.
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$\cdot$m and the max error of the joint estimated torque to 0.34N$\cdot$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.

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