Multilevel Control Strategy of Human–Exoskeleton Cooperative Motion With Multimodal Wearable Training Evaluation

Abstract

A multilevel control strategy is proposed for a lower limb exoskeleton to realize different human training modes. In the high-level control layer, the human training mode is decided by the operator’s motion intention to generate the reference gait trajectory. Meanwhile, both the joint estimation torque by the long short-term memory (LSTM) network and the human-exoskeleton interactive torques are used to evaluate the wearable comfort performance of the operator. In the middle-level control layer, a variable admittance controller is designed to plan three training modes of human-exoskeleton cooperative motion: passive, active, and passive-to-active mode (PAM). In the low-level control loop, a fixed-time convergent controller with radial basis function neural network (RBFNN) estimation law and input deadzone compensation is presented to guarantee the exoskeleton joint position tracks the desired trajectory of the admittance loop output. To avoid the Zeno phenomenon of the designed controller, an event-triggered mechanism (ETM) is used to determine the execution time for sampling and transmitting signals. Finally, the effectiveness of the proposed control strategy is verified by both simulation and experimental results.