Abstract
It is important to control the high accurate position and force to prevent unexpected accidents by a robot manipulator. Direct-drive robots are suitable to the position and force control with high accuracy, but it is difficult to design a controller because of the system's nonlinearity and link-interactions. This paper is concerned with the study of the stabilization force control of direct-drive robots. The proposed algorithm is consists of the feedback controllers and the neural networks. After the completion of learning, the outputs of feedback controllers are nearly equal to zero, and the neural networks play an important role in the control system. Therefore, the optimum adjustment of control parameters is unnecessary. In other words, the proposed algorithm does not need any knowledge of the controlled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the force control of a parallelogram link-type robot.