Differential evolution approach for tuning an H∞ controller in AMB systems

Alexander Smirnov; Rafal P. Jastrzebski

doi:10.1109/IECON.2009.5414744

Differential evolution approach for tuning an H_∞ controller in AMB systems

Alexander Smirnov, Rafal P. Jastrzebski

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

Abstract

In this paper a controller for an uncertain active magnetic bearing system is synthesized. A loop-shaping H_∞ approach is applied and the performance weights are selected to fulfill the frequency domain specifications. Weight selection is usually done from the engineering perspective and needs some experience. In this work, the selection is automated with a differential evolution algorithm. Objective values and constraints based on singular values of the system are applied. Single- and multiobjective algorithms are evaluated from the viewpoints of robustness and performance. The results show that the differential algorithm can improve all the objectives compared with a system tuned by a human operator.

Original language	English
Title of host publication	2009 35th Annual Conference of the IEEE Industrial Electronics Society (IECON)
Pages	1514-1518
ISBN (Electronic)	978-1-4244-4650-6, 978-1-4244-4649-0
DOIs	https://doi.org/10.1109/IECON.2009.5414744
Publication status	Published - 1 Dec 2009
MoE publication type	A4 Article in a conference publication
Event	35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009 - Porto, Portugal Duration: 3 Nov 2009 → 5 Nov 2009

Conference

Conference	35th Annual Conference of the IEEE Industrial Electronics Society, IECON 2009
Country	Portugal
City	Porto
Period	3/11/09 → 5/11/09

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10.1109/IECON.2009.5414744

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AB - In this paper a controller for an uncertain active magnetic bearing system is synthesized. A loop-shaping H∞ approach is applied and the performance weights are selected to fulfill the frequency domain specifications. Weight selection is usually done from the engineering perspective and needs some experience. In this work, the selection is automated with a differential evolution algorithm. Objective values and constraints based on singular values of the system are applied. Single- and multiobjective algorithms are evaluated from the viewpoints of robustness and performance. The results show that the differential algorithm can improve all the objectives compared with a system tuned by a human operator.

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