Improved recurrent neural networks for online solution of Moore-Penrose inverse applied to redundant manipulator kinematic control

In this paper, two novel neural networks (NNNs), namely NNN-L and NNN-R neural models, are proposed to online left and right Moore-Penrose inversion. As compared to GNN (gradient neural network) and the recently proposed ZNN (Zhang neural network) for the left or right Moore-Penrose inverse solving, our models are theoretically proven to possess superior global convergence performance. More importantly, the proposed NNN-R model is successfully applied to path-tracking control of a three-link planar robot manipulator. Illustrative examples well validate the theoretical analyses as well as demonstrate the feasibility of the proposed models, which are adopted and verified their effectiveness in kinematic control of a redundant manipulator, for real-time Moore-Penrose inverse solving.