Force tracking impedance control of robot by learning of robot-environment dynamics

로봇-작업환경 동역학의 학습에 의한 로봇의 힘 추종 임피이던스 제어

Performance of force tracking impedance control of robot manipulators is degraded by the uncertainties in the robot and environment dynamic model. The purpose of this paper is to improve the controller robustness by applying neural network. Neural networks are designed to learn the uncertainties in robot and environment model for compensating the uncertainties. The proposed scheme is verified through the simulation of 20DOF robot manipulator.