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Dynamic Linearization based Adaptive Iterative Learning Control for Functional Electrical Stimulation


S. Lombriser

Semester Thesis, FS14 (10360)

This report describes the implementation of four dierent control methods for functional electrical stimulation (FES) that can be applied in the rehabilitation of patients with paretic muscles. The controllers act on a simulated model of the knee joint. They are based on a control technique called Iterative Learning Control (ILC). The rst method is an ILC method intended for linear systems. This project evaluates its ability to control the nonlinear knee model. The remaining three methods are dynamic linearization techniques, which can handle nonlinear systems. An optimization problem yields the input signal for the system. This enables the controller to include input constraints, which are necessary for muscle stimulation. The simulation results show that the rst method yields relatively good tracking accuracy also for the nonlinear knee model. The remaining three methods are better in dealing with noise in the output measurement. Their convergence to a desired system output is faster.


Type of Publication:

(13)Semester/Bachelor Thesis

R. Nguyen

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2014:IFA_4841
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