Adaptive finite-time tracking control for a robotic manipulator with unknown deadzone

Abstract

This paper is concerned with the adaptive finite-time control for a robotic manipulator preceded by unknown non-symmetric deadzone. Radial basis function neural networks (RBFNNs) are employed to approximate the unknown dynamics and the deadzone effect of actuators. Adaptive finite-time tracking controller is then proposed based on the finite-time stability theorem in combination with backstepping technique. Consequently, tracking control of a robotic manipulator with finite-time convergent property is achieved even in the presence of unknown uncertainties and deadzone nonlinearity. Stability of the closed-loop system is analyzed via Lyapunov direct method. Simulation studies on a two-joint rigid manipulator are conducted to examine the effectiveness of the proposed control.