IEEE Trans Neural Syst Rehabil Eng. 2024;32:2589-2597. doi: 10.1109/TNSRE.2024.3429206. Epub 2024 Jul 22.
Pneumatic artificial muscle (PAM) has been widely used in rehabilitation and other fields as a flexible and safe actuator. In this paper, a PAM-actuated wearable exoskeleton robot is developed for upper limb rehabilitation. However, accurate modeling and control of the PAM are difficult due to complex hysteresis. To solve this problem, this paper proposes an active neural network method for hysteresis compensation, where a neural network (NN) is utilized as the hysteresis compensator and unscented Kalman filtering is used to estimate the weights and approximation error of the NN in real time. Compared with other inversion-based methods, the NN is directly used as the hysteresis compensator without needing inversion. Additionally, the proposed method does not require pre-training of the NN since the weights can be dynamically updated. To verify the effectiveness and robustness of the proposed method, a series of experiments have been conducted on the self-built exoskeleton robot. Compared with other popular control methods, the proposed method can track the desired trajectory faster, and tracking accuracy is gradually improved through iterative learning and updating.
气动人工肌肉（PAM）作为一种灵活、安全的执行器，已广泛应用于康复等领域。本文开发了一种由 PAM 驱动的可穿戴上肢康复外骨骼机器人。然而，由于复杂的迟滞现象，PAM 的精确建模和控制具有一定难度。为了解决这个问题，本文提出了一种用于迟滞补偿的主动神经网络方法，其中神经网络（NN）被用作迟滞补偿器，而无迹卡尔曼滤波用于实时估计 NN 的权重和逼近误差。与其他基于反演的方法相比，NN 直接用作迟滞补偿器，无需反演。此外，由于权重可以动态更新，因此该方法不需要对 NN 进行预训练。为了验证所提出方法的有效性和鲁棒性，在自制的外骨骼机器人上进行了一系列实验。与其他流行的控制方法相比，所提出的方法可以更快地跟踪期望轨迹，并且通过迭代学习和更新，跟踪精度逐渐提高。
