dc.contributor.author	Nguyen, Chuong Hoang	en
dc.contributor.committeechair	Leonessa, Alexander	en
dc.contributor.committeemember	Wicks, Alfred L.	en
dc.contributor.committeemember	Southward, Steve C.	en
dc.contributor.committeemember	Kurdila, Andrew J.	en
dc.contributor.committeemember	Abaid, Nicole	en
dc.contributor.department	Mechanical Engineering	en
dc.date.accessioned	2016-06-04T08:00:48Z	en
dc.date.available	2016-06-04T08:00:48Z	en
dc.date.issued	2016-06-03	en
dc.description.abstract	Functional Electrical Stimulation (FES) is a technique that applies electrical currents to nervous tissue in order to actively induce muscle contraction. Recent research has shown that FES provides a promising treatment to restore functional tasks due to paralysis caused by spinal cord injury, head injury, and stroke, to mention a few. Therefore, the overarching goal of this research work is to develop FES controllers to enable patients with movement-disorder to control their limbs in a desired manner and, in particular, to aid Parkinson's patients to suppress hand tremor. In our effort to develop strategies for muscle stimulation control, we first implement a model-based control technique assuming that all the states are measurable. The Hill-type muscle model coupled with a simplified 2DoF model of the arm is used to study the performance of our proposed adaptive sliding mode controller for simulation purpose. However, in the more practical situations, human limb dynamics are extremely complicate and it is inadequate to use model based controllers, especially considering there are still technical limitations that allow in vivo measurements of muscle activity. To tackle these challenges, we have developed output feedback adaptive control approaches for a class of unknown multi-input multi-output systems. Such control strategies are first developed for linear systems, and then extended to the nonlinear case. The proposed controllers, supported by experimental results, require minimum knowledge of the system dynamics and avoid many restrictive assumptions typically found in the literature. Therefore, we expect that the results introduced in this dissertation can provide a solution for a wide class of nonlinear uncertain systems, with focus on practical issues such as partial state measurement and the presence of mismatched uncertainties.	en
dc.description.degree	Ph. D.	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:7829	en
dc.identifier.uri	http://hdl.handle.net/10919/71312	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Unknown system dynamics	en
dc.subject	non-model based control	en
dc.subject	model predictor	en
dc.subject	nonlinear MIMO systems	en
dc.subject	output feedback control	en
dc.subject	adaptive control.	en
dc.title	Adaptive Predictor-Based Output Feedback Control of Unknown Multi-Input Multi-Output Systems: Theory and Application to Biomedical Inspired Problems	en
dc.type	Dissertation	en
thesis.degree.discipline	Mechanical Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Ph. D.	en

Adaptive Predictor-Based Output Feedback Control of Unknown Multi-Input Multi-Output Systems: Theory and Application to Biomedical Inspired Problems

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