Pneumatic muscle actuators ( PMAs ) are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries, such as strokes, spinal cord injuries, etc., to accomplish rehabilitation tasks. However, because PMAs have nonlinearities, hysteresis, and uncertainties, etc., complex mechanisms are rarely involved in the study of PMA-driven robotic systems. In this paper, we use nonlinear model predictive control ( NMPC ) and an extension of the echo state network called an echo state Gaussian process ( ESGP ) to design a tracking controller for a PMA-driven lower limb exoskeleton. The dynamics of the system include the PMA actuation and mechanism of the leg orthoses; thus, the system is represented by two nonlinear uncertain subsystems. To facilitate the design of the controller, joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP. A gradient descent algorithm is employed to solve the optimization problem and generate the control signal. The stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system dynamics. Simulations and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.

中文翻译：

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基于神经网络的气动肌肉执行器驱动外骨骼非线性模型预测跟踪控制

气动肌肉致动器（PMA）兼容并适用于已显示可有效协助神经系统损伤（例如中风，脊髓损伤等）的患者的机器人设备，以完成康复任务。但是，由于PMA具有非线性，磁滞和不确定性等特征，因此在PMA驱动的机器人系统的研究中很少涉及复杂的机制。在本文中，我们使用非线性模型预测控制（NMPC）和回波状态网络的扩展（称为回波状态高斯过程（ESGP））来设计用于PMA驱动的下肢外骨骼的跟踪控制器。该系统的动力学包括PMA致动和腿部矫形器的机制。因此，该系统由两个非线性不确定子系统表示。为了方便控制​​器的设计，基于ESGP的通用逼近能力来预测腿部矫形器的关节角度。梯度下降算法用于解决优化问题并生成控制信号。当ESGP能够近似系统动态时，可以确保闭环系统的稳定性。进行仿真和实验以验证ESGP的逼近能力并实现对四个健康受试者的步态模式训练。