Learning compliance control of robot manipulators in contact with the unknown environment

In this paper, a control mechanism is presented to improve the compliance performance of the robot manipulator when it is in contact with the unknown environment. This mechanism guarantees that the dynamics of the whole system follows a target impedance model so that the desired compliance performance is obtained. In particular, under the assumption that the dynamics is periodic, a learning law is developed to estimate the unknown dynamics parameters. Besides, the proposed systematic learning control does not require the accurate force measurement. Its performance and robustness are discussed in details by the rigorous analysis. At last, the validity of the proposed method is verified by simulation.