Abstract
Direct-drive robots are suitable to position and force control with high accuracy, but it is difficult to design a controller which gives satisfactory perfonnance because of the system's nonlinearity and link-interactions. This paper is concerned with the force control of direct-drive robots. The pro¬posed algorithm consists of feedback controllers and a neural network. Mter the completion of learning, the outputs of feedback controllers are nearly equal to zero, and the neural network con¬troller plays an important role in the control system. Therefore, the optimum adjustment of parameters of feedback controllers is unnecessary. In other words, the proposed algorithm does not need any knowledge of the controlled system in advance. The effectiveness of the proposed algo¬rithm is demonstrated by the experiment on the force control of a parallelogram link-type direct¬drive robot.