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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.
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Robust Pole Assignment Using Interval Polynomials
TL;DR: In this article, the authors present the application of new interval polynomial results to die design of pole assignment controllers, which allows controllers to make use of freedom in the placement of closed-loop poles to achieve increased robustness against plant parameter uncertainties.
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Coherent-Classical Estimation versus Purely-Classical Estimation for Linear Quantum Systems
TL;DR: In this article, a coherent-classical estimation scheme for a class of linear quantum systems is proposed, which consists of an estimator that is a mixed quantumclassical system without involving coherent feedback, yielding a classical estimate of a variable for the quantum plant.
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Robust adaptive h∞ control using integral quadratic constraints
TL;DR: In this paper, a bilinear nonlinearity is used to model the presence of unknown parameters in a robust adaptive H ∞ control system, which is achieved using a special integral quadratic constraint.
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On the Structure of Quantum Linear Systems
TL;DR: In this paper, the authors characterized the structure of quantum linear systems by means of a scattering representation, by which a relation between two systems $G$ and $Sigma$ is established, and they showed that the system G$ is lossless bounded real if and only if σ$ = lossless positive real.
Proceedings ArticleDOI
Robust smoothing for continuous time uncertain nonlinear systems
TL;DR: This paper presents the derivation of a robust smoothing algorithm for a class of uncertain nonlinear systems described in terms of an integral quadratic constraint which provides for a rich class of uncertainties.