A Tube-based Model Predictive Control Method to Regulate a Knee Joint with Functional Electrical Stimulation and Electric Motor Assist

IEEE Trans Control Syst Technol, 2021 · DOI: 10.1109/tcst.2020.3034850 · Published: September 1, 2021

Simple Explanation

This paper presents a control method for a hybrid neuroprosthesis system, which combines functional electrical stimulation (FES) and an electric motor to assist knee movement. The goal is to achieve optimal coordination between FES and the electric motor. The control method developed is a tube-based model predictive control (MPC), designed to be robust against uncertainties in the system model. The method uses an external feedback control to minimize the difference between the actual and desired knee positions. Experiments were conducted with both an able-bodied participant and a participant with spinal cord injury. The experimental results validated the controller’s ability to effectively allocate control inputs between FES and the electric motor, while also demonstrating robustness to modeling uncertainties.

Study Duration

Not specified

Participants

An able-bodied participant and a participant with spinal cord injury

Evidence Level

Not specified

Key Findings

1
The tube-based MPC method was proven to have recursive feasibility, compliance to input constraints, and exponentially bounded stability, suggesting its suitability for real-time control applications.
2
Experimental results validated the controller's ability to allocate control inputs to FES and the electric motor effectively, showcasing robust coordination in a hybrid neuroprosthesis system.
3
The experimental results also demonstrated the method's robustness to modeling uncertainties, which is crucial for practical implementation in real-world scenarios where precise models are difficult to obtain.

Research Summary

This paper introduces a tube-based model predictive control (MPC) method for a hybrid neuroprosthesis, combining functional electrical stimulation (FES) and an electric motor to regulate knee joint movement. The method aims to achieve robust and optimal coordination between FES and the motor, addressing the challenges posed by modeling uncertainties. The developed tube-based MPC method uses an external feedback control to limit the error between the actual and MPC-computed nominal position. It is proven to have recursive feasibility, compliance to input constraints, and exponentially bounded stability. Experimental results from tests on an able-bodied participant and a participant with spinal cord injury validate the controller’s ability to allocate control inputs to FES and the electric motor and the method’s robustness to modeling uncertainties, highlighting its potential for real-time application.

Practical Implications

Enhanced Rehabilitation Technologies

The tube-based MPC method offers a robust and effective control strategy for hybrid neuroprostheses, potentially improving rehabilitation outcomes for individuals with paraplegia.

Real-Time Control Implementation

The recursive feasibility and stability of the method make it suitable for real-time control applications, allowing for practical implementation in clinical settings.

Personalized Control Strategies

The method's ability to allocate control efforts between FES and electric motor can be tailored to individual patient needs, optimizing the balance between muscle stimulation and motor assistance.

Study Limitations

1
The study involves a limited number of participants, including only one participant with SCI, which may limit the generalizability of the findings.
2
The dynamic model parameter identification process, while thorough, can be time-consuming (40-90 minutes per leg), posing a practical challenge for clinical implementation.
3
The paper focuses on knee joint regulation during a seated leg extension task; further research is needed to evaluate its effectiveness in more complex movements such as walking and standing.

Your Feedback

Was this summary helpful?

Back to Assistive Technology