Periodic event-triggered sliding mode control for lower limb exoskeleton based on human-robot cooperation

This paper presents a periodic event-triggered sliding mode control (SMC) scheme based on humanrobot cooperation for lower limb exoskeletons. Firstly, a Genetic Algorithm-Back propagation (GA-BP) neural network is proposed to estimate the motion intention of the wearer through electromyography (EMG) signals. Secondly, the periodic event-triggered SMC strategy based on tanh function is designed to ensure the asymptotic convergence of the exoskeleton system and save communication resources, where the detailed expressions of sampling period and control gain are designed. Finally, comparative simulation and experimental analysis is presented to verify the effectiveness of the proposed control method. (C) 2021 Published by Elsevier Ltd on behalf of ISA.

Automated summary

This paper presents a periodic event-triggered sliding mode control scheme based on human-robot cooperation for lower limb exoskeletons. A Genetic Algorithm-Back propagation neural network is proposed to estimate the wearer's motion intention using electromyography signals. The designed strategy ensures convergence of the exoskeleton system and saves communication resources. Comparative simulation and experimental analysis verify the effectiveness of the proposed control method.