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Ian R. Petersen
Researcher at Australian National University
Publications - 992
Citations - 24919
Ian R. Petersen is an academic researcher from Australian National University. The author has contributed to research in topics: Quantum & Robust control. The author has an hindex of 67, co-authored 959 publications receiving 22649 citations. Previous affiliations of Ian R. Petersen include University of Cambridge & University of Manchester.
Papers
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Proceedings ArticleDOI
Convergence to Periodic Regimes in Nonlinear Feedback Systems with a Strongly Convex Backlash
TL;DR: In this article, the authors considered a class of nonlinear systems consisting of a linear part with an external input and a nonlinear feedback with a backlash, and established estimates for the Lyapunov exponents which quantify the rate of convergence of the system trajectories to a forced periodic regime when the input is a periodic function of time.
Journal ArticleDOI
Negative Imaginary State Feedback Equivalence for a Class of Nonlinear Systems
TL;DR: In this article , the necessary and sufficient conditions under which a class of nonlinear systems are state feedback equivalent to nonlinear negative imaginary (NI) systems with positive definite storage functions were investigated.
Journal ArticleDOI
A Coherent LQG approach to Quantum Equalization
TL;DR: In this article , a suboptimal, coherent quantum LQG controller is designed to solve a quantum equalization problem, which involves reformulating the problem as a control problem and then designing a classical LQGM controller and implementing it as a quantum system.
Proceedings ArticleDOI
Optimal stabilization of linear systems via decentralized feedback
Andrey V. Savkin,Ian R. Petersen +1 more
TL;DR: The main result of the paper presents a scheme for constructing such a collection of decentralized controllers which stabilizes the system and is optimal with respect to a quadratic performance index.
Posted Content
Towards Online Optimization for Power Grids
TL;DR: In this paper, the authors discuss potential advantages in extending distributed optimization frameworks to enhance support for power grid operators managing an influx of online sequential decisions, and discuss the connection and difference between offline and online distributed optimization.