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

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.

• 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.