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.

位置控制器用于自由运动，而力控制器用于机器人机械手的约束运动。混合动力控制器根据操纵器是否与环境接触，在位置和力控制模式之间切换。为了提高生产效率，操纵器与环境之间的接触速度不设置为零。但是，由于非零接触速度而产生的高冲击力可能会损坏环境表面或操纵器。在本文中，我们提出了一种虚拟的半主动阻尼学习方法，以在不降低接触速度的情况下抑制力过冲。虚拟半主动阻尼根据力控制中的机械手位置进行调整。有限内存BFGS方法用于获得未知环境的理想阻抗模型。通过最小化定义的成本函数，我们可以获得所需的交互性能。通过仿真和实验验证了该方法的正确性和有效性。