Neural network adaptive command filtered control of robotic manipulators with input saturation

Abstract

This paper investigates finite-time control of uncertain robotic manipulators with external disturbances by means of neural network control and backstepping technique. To solve the “explosion of terms” in traditional backstepping control, a second-order command filter is designed, and the virtual input and its first-order derivative can be obtained accurately in a finite time. The parameters of the neural network are updated by using the tracking error signals. The proposed controller can guarantee that the tracking error converges to a small region of the origin in some finite time. Finally, we give a simulation study to show the effectiveness of the proposed method.