An RBF neural network-based nonsingular terminal sliding mode controller for robot manipulators

Abstract

In this paper, a nonsingular terminal sliding mode controller (NTSM) based on radial basis function neural network (RBFNN) is proposed for rigid robot manipulator which has the parametric uncertainties. Terminal sliding mode controller can provide faster convergence and higher precision control compared with conventional sliding mode control. Therefore, it's a promising control approach for robot manipulator. Meanwhile, in order to compensate the parametric uncertainties, we use the RBFNN which has the capability to approximate any nonlinear function at arbitrary precision to learn the upper bound of them. The proposed controller requires no prior knowledge of the upper bound of the parametric uncertainties, and it's also robust to the external disturbance. Moreover, both finite time convergence and stability of the closed loop system can be guaranteed by Lyapunov theory. Finally, simulation results are presented to illustrate the effectiveness of the proposed controller.