Improved adaptive neural network control for humanoid robot hand in workspace

Abstract

In order to improve the control performance of humanoid robot hand in workspace, an adaptive control method based on improved neural network was proposed. rival-penalized competitive learning and recursive orthogonal least-squares algorithms were applied to reinforce the learning capability of Gaussian radial basis function neural network and realize the real-time of neural network. Moreover, an improved neural network model for humanoid robot hand was established with Ge-Lee matrix and its operator, and a controller was designed. Finally, an example of humanoid robot hand finger was provided. The results showed that the proposed control method could effectively control the unknown nonlinear dynamic properties and load disturbances of the finger with a much smaller tracking errors.