RNN-Based cooperative motion control of 2-dof Robot Arms

This paper presents a general recurrent neural network (RNN) model for online control of time-varying robot manipulators. The robot manipulators with different setting parameters work cooperatively on an unknown curve tracing. Each joint of the manipulator is respectively provided a learning method to optimize trajectory by the training RNN model. In this paper, the proposed RNN model shortens the period of learning and improves the cooperative accuracy of the existing neural networks for solving problems such as cutting or welding special types of products. A More complicated construction is to fit it for online cooperation. Simulation results show the effectiveness of this approach and that the proposed RNN model can successfully learn the inverse dynamics of robot manipulators as well as perform accurate tracking for a general trajectory. It is also shown that the proposed method is better than the conventional method due to its improved evolution functions.