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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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Approximation and stabilization of distributed systems by lumped systems
TL;DR: In this article, a complete answer to the question in terms of the behaviour of the purely atomic parts of the stable numerator and denominator of the plant is given. And in each case, it is shown that the system can be stabilized by a linear time-invariant distributed system.
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Stabilization of stiff formations with a mix of direction and distance constraints
TL;DR: This paper looks at the design of a distributed control scheme to solve the mixed constraint formation control problem with an arbitrary number of agents and a gradient control law is proposed based on the mathematical notion of a stiff formation structure and a corresponding stiff constraint matrix.
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Application of the Second Method of Lyapunov to the Proof of the Markov Stability Criterion
TL;DR: In this paper, a number of approaches to check the roots of polynomial polynomials to determine whether they have negative real parts are discussed, and the stability criterion in terms of Markov determinants is related to the Hermite criterion with the aid of Lyapunov theory.
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Brief Closed-loop identification with an unstable or nonminimum phase controller
TL;DR: It is shown that the internal stability of the resulting model, in closed loop with the same controller, is not always guaranteed if this controller is unstable and/or nonminimum phase, and that the classical closed-loop prediction-error identification methods present different properties regarding this stability issue.
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Forwards, backwards, and dynamically reversible Markovian models of second-order processes
TL;DR: Situations in which related causal and anticausal models have the same transfer function matrix are examined in detail, and the models are shown to possess internal and external time-reversiblity properties.