N
Naira Hovakimyan
Researcher at University of Illinois at Urbana–Champaign
Publications - 512
Citations - 11345
Naira Hovakimyan is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Adaptive control & Control theory. The author has an hindex of 48, co-authored 476 publications receiving 10255 citations. Previous affiliations of Naira Hovakimyan include Virginia Tech & Wentworth Institute of Technology.
Papers
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Book
L1 adaptive control theory : guaranteed robustness with fast adaptation
Naira Hovakimyan,Chengyu Cao +1 more
TL;DR: This book presents a comprehensive overview of the recently developed L1 adaptive control theory, including detailed proofs of the main results and also presents the flight test results that have used this theory and contains results not yet published in technical journals and conference proceedings.
Journal ArticleDOI
Adaptive output feedback control of nonlinear systems using neural networks
TL;DR: A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation, and extends the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data.
Posted Content
Design and Analysis of a Novel $\mathcal{L}_1$ Adaptive Control Architecture with Guaranteed Transient Performance
Chengyu Cao,Naira Hovakimyan +1 more
TL;DR: A novel adaptive control architecture that adapts fast and ensures uniformly bounded transient response for system's both signals, input and output, simultaneously is presented, which relies on the small-gain theorem for the proof of asymptotic stability.
Journal ArticleDOI
Design and Analysis of a Novel ${\cal L}_1$ Adaptive Control Architecture With Guaranteed Transient Performance
Chengyu Cao,Naira Hovakimyan +1 more
TL;DR: In this paper, a low-pass filter in the feedback loop is proposed to ensure uniformly bounded transient response for system's both signals, input and output simultaneously, and the small-gain theorem is used for the proof of asymptotic stability.
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Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks
TL;DR: It is argued that it is sufficient to build an observer for the output tracking error of uncertain nonlinear systems to ensureUltimate boundedness of the error signals is shown through Lyapunov's direct method.