School of Optoelectronic Science and Engineering, Soochow University, Suzhou 215031, China.
School of Electronic & Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Sensors (Basel). 2024 Feb 26;24(5):1505. doi: 10.3390/s24051505.
The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human-machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs.
下肢增强型外骨骼机器人的扭矩曲线选择是外骨骼机器人控制中的一个关键问题。作为人机耦合系统，从传感器数据到关节扭矩的映射非常复杂且非线性，很难使用数学工具进行准确建模。在这项研究中，使用光学运动捕捉系统和三维测力台获得了外骨骼机器人爬楼梯时的膝关节扭矩数据，并同时采集了外骨骼机器人下肢的惯性测量单元 (IMU) 数据。可以使用机器学习方法学习非线性逼近。在这项研究中，使用结合了 CNN 和 LSTM 的多变量网络模型进行非线性回归预测，并获得了膝关节扭矩控制模型。由于机械传动、通信和底部控制器的延迟，实际扭矩曲线会滞后于理论曲线。为了补偿这些延迟，在模型训练阶段对扭矩曲线进行了不同的时间移位，以产生不同的控制模型。将上述模型应用于一种轻型膝关节外骨骼机器人。通过表面肌电图 (sEMG) 实验评估了外骨骼机器人的性能，并比较了不同时间移位参数对性能的影响。在测试过程中，股直肌 (RF) 的 sEMG 活动降低了 20.87%，而股中间肌 (VM) 的 sEMG 活动增加了 17.45%。实验结果验证了这种控制模型在辅助膝关节爬楼梯方面的有效性。
