Cooperative Control of Two Direct-Drive Robots Using Neural Networks. Grasping and Movement of an Object.

Abstract

A learning control algorithm using neural networks is proposed for the grasping and the movement of an object by a pair of direct-drive (DD) robots of two degrees of freedom. The proposed algorithm has three feedback controllers and two neural networks. After the completion of learning, the outputs of the feedback controllers are nearly equal to zero, and the two neural networks play an important role in the control system. Therefore, the optimum setting of control parameters is unnecessary. In other words, the proposed algorithm does not necessitate any knowledge of the controlled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the cooperative control of the parallelogram-type DD robots. It is also shown that gravity can be compensated by this algorithm.