Intelligent control of space robot system using RBF neural network

In this paper, an intelligent controller is proposed for a space robot system with an attitude controlled base without joint acceleration measurements. The controller consists of computed torque type part, RBF neural network and an adaptive controller. The controller achieves the required tracking effectively. The controller is able to learn the existing structured and unstructured uncertainties in the system in online manner. The RBF network learns the unknown part of the space robot system dynamics with no requirement of the offline training. The adaptive controller is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable in the sense of Lyapunov. Finally numerical simulation studies are performed to evaluate the controller performance.