Zhao Zhirui, Hou Xinyu, Shan Dexing, Liu Hongjun, Liu Hongshuai, Hao Lina
School of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Suzhou Automotive Research Institute, Tsinghua University, Beijing 100190, China.
Polymers (Basel). 2024 Dec 18;16(24):3533. doi: 10.3390/polym16243533.
In this study, a fuzzy adaptive impedance control method integrating the backstepping control for the PAM elbow exoskeleton was developed to facilitate robot-assisted rehabilitation tasks. The proposed method uses fuzzy logic to adjust impedance parameters, thereby optimizing user adaptability and reducing interactive torque, which are major limitations of traditional impedance control methods. Furthermore, a repetitive learning algorithm and an adaptive control strategy were incorporated to improve the performance of position accuracy, addressing the time-varying uncertainties and nonlinear disturbances inherent in the exoskeleton. The stability of the proposed controller was tested, and then corresponding simulations and an elbow flexion and extension rehabilitation experiment were performed. The results showed that, with the proposed method, the root mean square of the tracking error was 0.032 rad (i.e., 21.95% less than that of the PID method), and the steady-state interactive torque was 1.917 N·m (i.e., 46.49% less than that of the traditional impedance control). These values exceeded those of the existing methods and supported the potential application of the proposed method for other soft actuators and robots.
在本研究中，为便于机器人辅助康复任务，开发了一种将反步控制集成到气动人工肌肉（PAM）肘部外骨骼的模糊自适应阻抗控制方法。所提出的方法使用模糊逻辑来调整阻抗参数，从而优化用户适应性并降低交互扭矩，而这是传统阻抗控制方法的主要局限性。此外，还引入了重复学习算法和自适应控制策略，以提高位置精度性能，解决外骨骼固有的时变不确定性和非线性干扰问题。对所提出控制器的稳定性进行了测试，然后进行了相应的仿真以及肘部屈伸康复实验。结果表明，采用所提出的方法，跟踪误差的均方根为0.032弧度（即比PID方法小21.95%），稳态交互扭矩为1.917牛·米（即比传统阻抗控制小46.49%）。这些数值超过了现有方法的数值，并支持了所提出方法在其他软驱动器和机器人上的潜在应用。
