Sub-optimally Solving Actuator Redundancy in a Hybrid Neuroprosthetic System with a Multi-layer Neural Network Structure

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

A hybrid neuroprosthesis system is a promising rehabilitation technology to restore lower-limb function in persons with paraplegia. The technology combines functional electrical stimulation (FES) and a powered lower limb exoskeleton to produce movements for walking and standing. The main control challenge in the hybrid neuroprosthesis is to achieve an optimal coordination between FES and electric motors. Model-based optimal control methods have been suggested for the control of the hybrid neuroprosthesis. However, it is often difficult to effect robust control performance with model-based optimal control methods due to modeling uncertainties. A tube-based model predictive control (MPC) method is developed to obtain robust and optimal coordination between FES and an electric motor during a knee regulation task. An external feedback control is used to limit the error between the actual position and the MPC-computed nominal position. The tube-based MPC method is proven to have recursive feasibility, compliance to input constraints, and exponentially bounded stability. The experimental results obtained from 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 as well as method's robustness to modeling uncertainties.