SEMG data driven-based anti-disturbance control enables adaptive interaction of lower limb rehabilitation exoskeleton

Abstract

The efficient human–machine interaction control is the core of human–machine integration, which can improve the tracking accuracy of the lower limb rehabilitation exoskeleton system and the compliance of human–machine cooperative movement. However, the non-ideal factors such as mechanical friction, model uncertainties, iteration errors and external interference in the training process undoubtedly bring potential dangers to the safety of rehabilitation training. Consequently, to escape unnecessary damage caused by external disturbances (especially the unbounded disturbances) during rehabilitation training, a noise-suppressing zeroing neural network human–machine interaction controller is developed in this work, which is based on the constructed human–machine coupling dynamic model and the active motion intention (active torque) of the subject identified by exploiting the deep convolutional neural network. The simulation experiments and statistical analyses verify that the present noise-suppressing zeroing neural network controller can be applied to monitor the lower limb rehabilitation exoskeleton for assisting the subjects in various task with the external disturbances,and the average root mean square error of the hip, knee and ankle joints’ angles are 0.0015rad, 0.0051rad and 0.0056rad, respectively. Furthermore, a novel model predictive control is developed and analyzed based on noise-suppressing zeroing neural network controller, which can effectively constrain the angle and angular velocity of the lower limb rehabilitation exoskeleton. The root mean square errors of hip, knee and ankle joint angle are 0.0032rad, 0.0078rad, and 0.0085rad, respectively. Finally, the platform experiment verifies that the proposed controllers can be utilized to control the lower limb rehabilitation exoskeleton to assist the subjects in rehabilitation training.