Neuro-Adaptive-Based Predefined-Time Smooth Control for Manipulators With Disturbance
Yanli Fan, Chenguang Yang, Hong Zhan, Yongming Li
Abstract
In this article, an adaptive neural network (NN) predefined-time tracking control strategy is investigated for robot systems with external disturbance. First, under the predefined-time stability criterion, a new time-controlled torque controller is constructed, which allows for the system convergence time to be set beforehand. This is conducive to manipulators performing trajectory tracking tasks that require specific convergence times. In addition, the continuous terms are constructed by smoothly switching between the fractional and cubic terms of state-dependence. This solution successfully resolves the issues of singularity. Moreover, in order to compensate for unknown nonlinearity and torque disturbance, two different adaptive update laws are established, respectively. Furthermore, rigorous stability is proved based on the predefined-time Lyapunov theory. Finally, the accuracy and efficiency of the NN-based predefined-time control algorithm is confirmed and validated through both numerical simulations and practical experiments conducted with the Baxter robot.