This article investigates the predefined time trajectory tracking control problem of upper limb rehabilitation robots in the presence of the model uncertainty and external disturbances. An adaptive neural network-based practical predefined-time fast nonsingular terminal sliding-mode control (PPT-FNTSMC) strategy is proposed, in which a novel sliding mode surface is constructed to achieve predefined-time convergence and avoid the singularity problem as well. Additionally, a neural network is employed to approximate the lumped disturbances, and the estimated values are utilized in the controller for compensation, thereby reducing the required switching gain and mitigating chattering phenomenon. A rigorous theoretical analysis is provided to illustrate that the tracking errors can converge to a vicinity of the origin in a predefined time. Finally, simulations on a two-joint single-arm robot and a 5-DOF upper-limb exoskeleton are conducted and compared with an existing control method to demonstrate the effectiveness and superiority of the proposed control strategy.

中文翻译：

---

基于自适应神经网络的上肢康复机器人实用预定义时间非奇异终端滑模控制


本文研究了存在模型不确定性和外部干扰的情况下上肢康复机器人的预定时间轨迹跟踪控制问题。提出了一种基于自适应神经网络的实用预定义时间快速非奇异终端滑模控制（PPT-FNTSMC）策略，该策略构造了一种新颖的滑模面，以实现预定义时间收敛并避免奇异性问题。此外，采用神经网络来近似集总扰动，并将估计值用于控制器中进行补偿，从而降低所需的开关增益并减轻颤振现象。提供了严格的理论分析来说明跟踪误差可以在预定时间内收敛到原点附近。最后，对二关节单臂机器人和五自由度上肢外骨骼进行仿真，并与现有控制方法进行比较，验证了所提出控制策略的有效性和优越性。