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

In this paper, a direct uncertainty minimization framework is developed and demonstrated for model reference adaptive control laws. The proposed framework consists of a novel architecture involving modification terms in the adaptive control law and the update law. In particular, these terms are const...

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Bibliographic Details
Published in:Machines (Basel) 2017-03, Vol.5 (1), p.9
Main Authors: Gruenwald, Benjamin, Yucelen, Tansel, Muse, Jonathan
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive systems
Closed loops
Control theory
Feedback control
Model reference adaptive control
Nonlinear control
Optimization
Uncertainty
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Summary:In this paper, a direct uncertainty minimization framework is developed and demonstrated for model reference adaptive control laws. The proposed framework consists of a novel architecture involving modification terms in the adaptive control law and the update law. In particular, these terms are constructed through a gradient minimization procedure in order to achieve improved closed-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.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines5010009