Virtual semi-active damping learning control for robot manipulators interacting with unknown environment

Abstract

Position controllers are used for free motion, whereas force controllers are used for constrained motion of robotic manipulators. The hybrid controller switches between position and force control modes depending on whether the manipulator is in contact with the environment. To improve production efficiency, the velocity of contact between the manipulator and environment is not set to zero. However, the high impact force due to the nonzero contact velocity might damage the environment surface or manipulators. In this article, we propose a virtual semi-active damping learning method to suppress force overshoot without decreasing the contact velocity. Virtual semi-active damping is adjusted according to the manipulator position in force control. The limited-memory BFGS method is used to obtain the ideal impedance model for the unknown environment. By minimizing the defined cost function, we get the desired interaction performance. The correctness and effectiveness of the proposed method are verified by conducting simulations and experiments.