Enhancing precision performance of trajectory tracking controller for robot manipulators using RBFNN and adaptive bound

In this paper the design issues of trajectory tracking controller for robot manipulators are considered. The performance of classical model based controllers is reduced due to the presence of inherently existing uncertainties in the dynamic model of the robot manipulator. An intermediate approach between model based controllers and neural network based controllers is adopted to enhance the precision of trajectory tracking. The performance of the model based controller is enhanced by adding an RBF neural network and an adaptive bound part. 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 bound part is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable. Finally, the numerical simulation results are produced with various controllers and the effectiveness of the proposed controller is shown in a comparative study for the case of a Microbot type robot Manipulator.