A Distributed Automatic Control Framework for Simultaneous Control of Torque and Cadence in Functional Electrical...

Area of Science:

• Rehabilitation Engineering
• Biomedical Control Systems
• Neuroprosthetics

Background:

• Functional electrical stimulation (FES) cycling faces challenges with automatic control due to unknown disturbances and time-varying muscle dynamics.
• Previous FES control methods require system modeling and pre-adjusted parameters, limiting clinical application and performance.
• Optimizing stimulation patterns is crucial for effective FES cycling.

Purpose of the Study:

• To propose a novel, model-free distributed cooperative control framework for FES cycling.
• To develop an automatic stimulation pattern generator for precise muscle activation.
• To improve the efficiency, tracking accuracy, and endurance of FES cycling in paraplegic patients.

Main Methods:

• Implementation of an adaptive higher-order sliding mode (AHOSM) controller for simultaneous torque and cadence control.
• Development of an automatic pattern generator to determine muscle stimulation regions and gains.
• Validation through simulation studies and experiments on three spinal cord injury patients.

Main Results:

• The proposed AHOSM controller achieved significantly higher efficiency and tracking accuracy compared to a fixed-pattern HOSM controller.
• Average cadence and torque tracking errors were reduced to 5.77±0.5% and 5.23±0.8%, respectively.
• Reduced power consumption was observed, potentially leading to decreased muscle fatigue and increased cycling endurance.

Conclusions:

• The model-free AHOSM control framework offers a robust solution for FES cycling, adaptable to unknown disturbances.
• The automatic pattern generator enhances control precision and personalization for FES cycling.
• This advanced control strategy shows significant promise for improving motor-assisted FES cycling in individuals with spinal cord injuries.