Simulation-based Optimum Sensor Selection Design for an Uncertain EMS System via Monte-Carlo Technique

Konstantinos Michail; Argyrios C. Zolotas; Roger M. Goodall

doi:10.3182/20110828-6-IT-1002.01224

Simulation-based Optimum Sensor Selection Design for an Uncertain EMS System via Monte-Carlo Technique

Konstantinos Michail, Argyrios C. Zolotas, Roger M. Goodall

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

Optimum sensor selection in control system design is often a non-trivial task to do. This paper presents a systematic design framework for selecting the sensors in an optimum manner that simultaneously satisfies complex system performance requirements such as optimum performance and robustness to structured uncertainties. The framework combines modern control design methods, Monte Carlo techniques and genetic algorithms. Without losing generality its efficacy is tested on an electromagnetic suspension system via appropriate realistic simulations.

Original language	English
Pages (from-to)	12650-12655
Number of pages	6
Journal	IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume	44
Issue number	1
DOIs	https://doi.org/10.3182/20110828-6-IT-1002.01224
Publication status	Published - Jan 2011
Externally published	Yes
Event	18th World Congress: The International Federation of Automatic Control - Milano, Italy Duration: 28 Aug 2011 → 2 Sep 2011 Conference number: 18

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abstract = "Optimum sensor selection in control system design is often a non-trivial task to do. This paper presents a systematic design framework for selecting the sensors in an optimum manner that simultaneously satisfies complex system performance requirements such as optimum performance and robustness to structured uncertainties. The framework combines modern control design methods, Monte Carlo techniques and genetic algorithms. Without losing generality its efficacy is tested on an electromagnetic suspension system via appropriate realistic simulations.",

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N1 - Conference code: 18

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AB - Optimum sensor selection in control system design is often a non-trivial task to do. This paper presents a systematic design framework for selecting the sensors in an optimum manner that simultaneously satisfies complex system performance requirements such as optimum performance and robustness to structured uncertainties. The framework combines modern control design methods, Monte Carlo techniques and genetic algorithms. Without losing generality its efficacy is tested on an electromagnetic suspension system via appropriate realistic simulations.

KW - EMS systems

KW - Genetic algorithms

KW - Modern control design

KW - Monte Carlo

KW - Optimum sensor selection

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Y2 - 28 August 2011 through 2 September 2011

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Simulation-based Optimum Sensor Selection Design for an Uncertain EMS System via Monte-Carlo Technique

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