Adaptive Neural Network Control for Multi-fingered Robot Hand Manipulation in the Constrained Environment

This note presents a robust adaptive neural network (NN) control scheme for multi-fingered robot hand manipulation system in the constrained environment to achieve arbitrarily small motion and force tracking errors. The controllers consist of the model-based controller, the NN controller and the robust controller. The model-based controller deals with the nominal dynamics of the manipulation system. The NN handles the unstructured dynamics and external disturbances. The NN weights are tuned online, without the offline learning phase. The robust controller is introduced to compensate for the effects of residual uncertainties. An adaptive law is developed so that no priori knowledge of the bounds for residual uncertainties is required. Most importantly, the exponential convergence properties for motion and force tracking are achieved.