Nonlinear modeling and adaptive control approach to functional electrical stimulation

This research is concerned with the application of system identification and adaptive control to Functional Electrical Stimulation. The work consists of developing a model which describes EMG (Electromyogram) activity to forearm motion. Although several EMG models presently exist, the goal was to produce a model more suitable for on-line applications while also taking into account the system nonlinearities. The parameters of this model were estimated using a least squares algorithm. The model was tested by simulation and experimentally collected data. The developed model explains well the forearm movement. From the developed model, an adaptive controller was designed using a model reference control scheme. This adaptive controller was used for generating the suitable stimulus pattern. The simulation results showed good tracking and indicated the controllers ability to adapt to changes in the arm's nonlinear gain.