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Direct Uncertainty Minimization Framework for System Performance Improvement in Model Reference Adaptive Control

1
Laboratory for Autonomy, Control, Information, and Systems (LACIS), Department of Mechanical Engineering, University of South Florida, 4202 East Fowler Ave., Tampa, FL 33620, USA
2
Air Force Research Laboratory, Wright Patterson Air Force Base, Dayton, OH 45433, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Dan Zhang
Machines 2017, 5(1), 9; https://doi.org/10.3390/machines5010009
Received: 3 January 2017 / Accepted: 28 February 2017 / Published: 8 March 2017
(This article belongs to the Special Issue Robotic Machine Tools)
Inthispaper, adirectuncertaintyminimizationframeworkisdevelopedanddemonstrated for model reference adaptive control laws. The proposed framework consists of a novel architecture involvingmodificationtermsintheadaptivecontrollawandtheupdatelaw. Inparticular,theseterms areconstructedthroughagradientminimizationprocedureinordertoachieveimprovedclosed-loop system performance with adaptive control laws. The proposed framework is first developed for adaptive control laws with linear reference models and then generalized to adaptive control laws with nonlinear reference models. Two illustrative numerical examples are included to demonstrate the efficacy of the proposed framework. View Full-Text
Keywords: model reference adaptive control; uncertain dynamical systems; transient performance; nonlinear reference models model reference adaptive control; uncertain dynamical systems; transient performance; nonlinear reference models
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Gruenwald, B.C.; Yucelen, T.; Muse, J.A. Direct Uncertainty Minimization Framework for System Performance Improvement in Model Reference Adaptive Control. Machines 2017, 5, 9.

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