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

Area of Science:

• Robotics
• Biomedical Engineering
• Control Systems

Background:

• Lower limb exoskeletons require advanced control for effective human-robot cooperation.
• Efficient control strategies are needed to manage communication load and ensure system stability.

Purpose of the Study:

• To develop a periodic event-triggered sliding mode control (SMC) scheme for lower limb exoskeletons.
• To improve human-robot interaction and reduce communication resource usage.

Main Methods:

• Utilized a Genetic Algorithm-Back propagation (GA-BP) neural network to interpret electromyography (EMG) signals for motion intention estimation.
• Designed a periodic event-triggered SMC strategy incorporating a tanh function for asymptotic system convergence.
• Optimized sampling periods and control gains for enhanced performance.

Main Results:

• The proposed GA-BP neural network accurately estimated wearer's motion intention from EMG signals.
• The event-triggered SMC strategy demonstrated asymptotic convergence of the exoskeleton system.
• Significant reduction in communication resources was achieved without compromising control performance.

Conclusions:

• The developed control method effectively integrates human intention with exoskeleton dynamics.
• The periodic event-triggered SMC approach offers a promising solution for efficient and stable lower limb exoskeleton control.
• This research validates the effectiveness through comparative simulations and experimental analysis.