Neural network-based compensation control for trajectory tracking of industrial robots

Abstract

In this paper, a novel dynamic compensatory strategy based on radial basis function (RBF) neural network robust H∞ controller is put forward for improving trajectory tracking accuracy of robot manipulators on account of modelling errors and external disturbances. The proposed strategy is effective to compensate the computed torque controller relied on precise dynamic model of robot, consisting of a RBF neural network, a variable structure controller and a robust H∞ controller. Furthermore, a velocity feed-forward with proposed method is employed to achieve higher trajectory tracking accuracy of end-effector and avoid the consideration of torque interface. The parameters of uncertain dynamics model are estimated by RBF neural network, while a variable structure controller is employed to make up the approximation errors of neural network. With the help of robust H∞ controller, effect of external disturbances will be attenuated and the robust tracking performance is achieved. Based on Lyapunov stability theorem, it is shown that the proposed controller can guarantee the stability of the control system and the desired performance of robotic system. The simulations and experiments on a multi-robot cutting manipulator indicate that the proposed compensatory strategy for industrial robot manipulator is superior to traditional computed torque control method in consideration of uncertainties.