Neuro-Adaptive-Based Predefined-Time Smooth Control for Manipulators With Disturbance
针对受外部扰动的机器人系统，提出一种自适应神经网络预定时间跟踪控制策略，通过构造时间控制扭矩控制器和连续项平滑切换，解决了奇异性和收敛时间预设问题，并在Baxter机器人上验证了有效性。
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.