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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/5258

Title: Optimised sensor configurations for a MAGLEV suspension system
Authors: Michail, Konstantinos
Zolotas, Argyrios C.
Goodall, Roger M.
Keywords: Sensor optimisation
MAGLEV suspensions
EMS optimisation
Genetic algorithms
Kalman filter
Evolutionary algorithms
Issue Date: 2008
Publisher: © University of Niš
Citation: MICHAIL, K., ZOLOTAS, A.C. and GOODALL, R.M., 2008. Optimised sensor configurations for a MAGLEV suspension system. Facta Universitatis Series: Mechanics, Automatic Control and Robotics, 7 (1), pp.169-184.
Abstract: This paper discusses a systematic approach for selecting the minimum number of sensors for an Electromagnetic suspension system that satisfies both optimised deterministic and stochastic performance objectives. The performance is optimised by tuning the controller using evolutionary algorithms. Two controller strategies are discussed, an inner loop classical solution for illustrating the efficacy of the evolutionary algorithm and a Linear Quadratic Gaussian (LQG) structure particularly on sensor optimisation.
Description: This article was published in the journal, Facta Universitatis Series: Mechanics, Automatic Control and Robotics [© University of Niš]. The original publication is available at: http://facta.junis.ni.ac.rs/macar/macar200801/macar200801-13.html
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/5258
ISBN: 0354–2009
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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