An adaptive control method for robot manipulators using radial basis function networks

Abstract

The neural network known as a sort of intelligent control strategy is used as a powerful tool of control systems since it has learning ability. But it is difficult for neural network controllers to guarantee the stability of control systems. In this paper we try connecting a radial basis function network to an adaptive control strategy. Radial basis function networks are simpler and easier to handle than multilayer perceptrons. We use the radial basis function network to generate control input signals that are similar to the control inputs of adaptive control using liner reparameterization of the robot manipulator. We adopt the signum function as an auxiliary controller. This paper also proves mathematically the stability of the control system under the existence of disturbances and modeling errors.