Fractional-Order Nonsingular Terminal Sliding Mode Control of Uncertain Robot Neural Network

Abstract

Aiming at the problem of low tracking accuracy and slow convergence speed of robot trajectory tracking control system with uncertainties and external disturbances, an adaptive fractional-order fast terminal sliding mode controller based on radial basis function (RBF) neural network is proposed. First of all, the method adopts nonsingular fast terminal sliding mode control, which makes the system converge to the equilibrium point in a limited time, and uses the fractional-order controller to improve the tracking performance of the controller. Moreover, we use RBF neural network to approximate unknown non-linear function of the system, and combine with adaptive compensation mechanism to realize model-free control. The stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, taking the manipulator as an example to verify the theory. The simulation results show that the proposed method can improve the tracking performance and system convergence speed, enhance the robustness to modeling errors and external disturbances, and weaken the chattering generated by the system.