Optimized Impedance Control of a Lightweight Gait Rehabilitation Exoskeleton Based on Accurate Knee Joint Torque Estimation

In recent years, with the increasing problem of an aging population, there has been a significant increase in the number of stroke patients presenting with motor dysfunction of the lower limbs. In this study, a knee exoskeleton rehabilitation robot driven by a quasi-direct driver actuator is designed. The torque generation model is constructed based on the TCN-LSTM hybrid neural network, and the knee joint torque is generated by sEMG and angle signal. A joint attention mechanism is introduced to enhance the accuracy of torque generation model. The impedance control parameters are adaptively adjusted in accordance with the joint torque. The experimental results demonstrate that the TCN-LSTM hybrid neural network is capable of effectively estimating torque, the mean MAE and CC of the proposed model are 1.141Nm and 93.7%, respectively. The optimized impedance control can optimize the initial value of the impedance parameter, which reduced the torque error by 5.54% and 50.64% at uphill tasks and walking task, respectively, and adaptively adjust the impedance parameter to ensure the coordination of the gait rehabilitation and the friendly human-robot interaction.