Adaptive neural sliding mode control of uncertain robotic manipulators with predefined time convergence

Abstract In this article, a predefined time convergence adaptive tracking control scheme is designed for a class of uncertain robotic manipulators with input saturation. First, a novel auxiliary dynamic system is proposed to handle the influence of input saturation. Radial basis function neural networks are used to approximate the uncertainty of the closed‐loop system and the neural adaptive law is designed by using the given time constant so that the neural networks have a fast convergence rate. The adaptive tracking controller is constructed by utilizing a nonsingular terminal sliding mode surface. Different from the finite‐time and the fixed‐time sliding mode control methods where the upper bound of the convergence time is related to system parameters, the convergence time upper bound of the proposed sliding mode controller is a given constant. Finally, numerical simulations are performed to illustrate that the proposed control scheme possesses the advantages of fast convergence rate and input saturation elimination.