Tracking control of redundant robot manipulators using RBF neural network and an adaptive bound on disturbances

In this paper, a hybrid trajectory tracking controller is designed for redundant robot manipulators, consisting of RBF neural network and an adaptive bound on disturbances. The controller is composed of computed torque type part, RBF neural network and an adaptive controller. The controller achieves end-effector trajectory tracking as well as subtask 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 robot 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 on a 3R planar robot manipulator to show the effectiveness of the control scheme.