This paper investigates an adaptive finite time control scheme for wearable exoskeletons to realize trajectory tracking control. The main feature of the proposed scheme is model-free in which no dynamic models are required except for the input and output data. Firstly, a second order ultra-local model is employed to replace the complex dynamic model of wearable exoskeleton as the controlled object, which is a novel designed model. In addition, a nonsingular fast terminal sliding surface is proposed to design the controller to guarantee the finite time convergence of tracking errors, and a radial basis function (RBF) neural network is developed to approximate the lumped disturbance in the ultra-local model. Then the stability of closed-loop system is proved by Lyapunov theory. Finally, to validate the proposed control scheme, virtual prototype is designed in SolidWorks and transferred to MATLAB, then visual simulation program is implemented based on SimMechanics. What’s more, reference trajectories are extracted from the measured data of DELSYS Electromyography (EMG) recording system. The effectiveness of proposed scheme is demonstrated by the simulation results.

中文翻译：

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基于超局部模型和径向基函数神经网络的可穿戴外骨骼自适应有限时间控制

本文研究了一种用于可穿戴外骨骼的自适应有限时间控制方案，以实现轨迹跟踪控制。所提出方案的主要特点是无模型，除了输入和输出数据外，不需要动态模型。首先，采用二阶超局部模型代替可穿戴外骨骼作为受控对象的复杂动态模型，是一种新颖的设计模型。此外，提出了一个非奇异的快速终端滑动面来设计控制器以保证跟踪误差的有限时间收敛，并开发了径向基函数（RBF）神经网络来近似超局部模型中的集中扰动。然后用李雅普诺夫理论证明了闭环系统的稳定性。最后，为了验证所提出的控制方案，在 SolidWorks 中设计虚拟样机并传输到 MATLAB，然后基于 SimMechanics 实现可视化仿真程序。此外，参考轨迹是从 DELSYS 肌电图 (EMG) 记录系统的测量数据中提取的。仿真结果证明了所提出方案的有效性。