Robust Trajectory Tracking Control of an Uncertain Robot with Time-varying Error Constrains

Abstract

In this article, a robustness adaptive trajectory tracking control strategy is proposed for a robot with uncertainties and error constraints. An asymmetric barrier Lyapunov function (BLF) is adopted to handle the problem of asymmetric time-varying error constraints and enhance the robustness of the control. The adaptive neural network (NN) is used to estimate the complex uncertainty dynamics. Moreover, consider the compensator to improve control accuracy. Using the proposed control strategy, asymptotic tracking can be implemented, and the tracking error does not violate the predefine boundary. All signals of the closed-loop system are proved to be semi-globally uniformly ultimately bounded (SGUUB) via Lyapunov analysis. The effectiveness of the proposed method is demonstrated by simulation results.