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Brian D. O. Anderson
Researcher at Australian National University
Publications - 1120
Citations - 50069
Brian D. O. Anderson is an academic researcher from Australian National University. The author has contributed to research in topics: Linear system & Control theory. The author has an hindex of 96, co-authored 1107 publications receiving 47104 citations. Previous affiliations of Brian D. O. Anderson include University of Newcastle & Eindhoven University of Technology.
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Quantification of frequency domain error bounds with guaranteed confidence level in prediction error identification
TL;DR: It is shown in this paper how to construct such frequency domain uncertainty set with a probability level of at least alpha, to identify prediction error identification of linearly parametrized models in the situation where the system is in the model set.
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A new robust control design procedure based on a PE identification uncertainty set
TL;DR: In this paper, a robust control design procedure based on a model and an uncertainty region deduced from classical PE identification is proposed, where the key step in the procedure is a quality assessment procedure for the pair "model-uncertainty region" taking into account the prescribed performance level.
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Scattering matrix synthesis via reactance extraction
TL;DR: In this paper, the problem of multiport passive network synthesis for a rational bounded real scattering matrix S(p) using state-space ideas was considered, and a symmetric symmetric scattering matrix with a minimum number of reactances was presented.
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Forwards and backwards models for finite-state markov processes
TL;DR: In this paper, the construction and properties of reversible and dynamically reversible models for Markov processes with rational power spectra with dynamically reversable reversals are studied. And the results on approximating Markov Processes with Rational Power Spectra (RPS) with dynamic reversability are given.
Proceedings ArticleDOI
On a hierarchical control strategy for multi-agent formation without reflection
TL;DR: A hierarchical control strategy which can be applicable to any number of agents based on the above type of potential function and a formation shaping incorporating a grouping of equilateral triangles, so that all controlled distances are in fact the same.