Adaptive Neural Tracking Control for Manipulators With Prescribed Performance Under Input Saturation

Abstract

In this article, an improved adaptive neural network (NN) nonsingular terminal sliding mode control (NTSMC) scheme is proposed for prescribed-performance trajectory tracking of manipulators with unmodeled dynamics and input saturation. In order to reduce the adverse effect of input saturation due to the conflict between excessive control force and limited motor torque, an auxiliary system is constructed. With the help of prescribed performance functions, we develop an improved NN-based NTSMC strategy to achieve tunable prescribed tracking errors under limited control, where it does not need prior precise knowledge of uncertainties. Theoretically, the uniform ultimate boundedness of the closed-loop system is proved by using the Lyapunov function. Finally, extensive comparative experiments are carried out on a ROKAE platform of a multidegree-of-freedom manipulator, and the improved tracking performance of the proposed scheme is verified.