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.
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Stability Margins of $\mathcal{L}_1$ Adaptive Controller: Part II
Chengyu Cao,Naira Hovakimyan +1 more
TL;DR: In this article, the stability margins of the adaptive control architecture were derived, including time delay and gain margins in the presence of time-varying bounded disturbance, and the stability of the system's both signals, input and output, were derived.
Offset Landings and Large Flight Envelope Modeling Work
TL;DR: In this article, the authors present new results of a flight test of the L1 adaptive control architecture designed to directly compensate for significant uncertain cross-coupling in nonlinear systems.
Journal ArticleDOI
A new characterization of stable neural network control for discrete-time uncertain systems
TL;DR: In this paper, a neuro adaptive control framework for discrete-time multivariable nonlinear uncertain systems is developed, which is Lyapunov-based and guarantees, instead of ultimate boundedness, partial asymptotic stability of the closed-loop system.
Proceedings ArticleDOI
Robust Vehicle Lane Keeping Control with Networked Proactive Adaptation
TL;DR: In this paper, a proactive robust adaptive control architecture for autonomous vehicles' lane-keeping control problems is proposed, where the data center generates a prior uncertainty estimate by combining weather forecasts and measurements from anonymous vehicles through a spatio-temporal filter.
Proceedings ArticleDOI
RRT Guided Model Predictive Path Integral Method
TL;DR: In this paper , an optimal sampling-based method was proposed to solve the real-time motion planning problem in static and dynamic environments, exploiting the Rapid-exploring Random Trees (RRT) algorithm and the Model Predictive Path Integral (MPPI) algorithm.