Surrogate assisted local search in PMSM drive design

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dc.contributor.author Neri, Ferrante en
dc.contributor.author Garcia, XdT en
dc.contributor.author Cascella, G. L. en
dc.contributor.author Salvatore, N. en
dc.date.accessioned 2012-08-13T11:30:43Z
dc.date.available 2012-08-13T11:30:43Z
dc.date.issued 2008-07
dc.identifier.citation Neri, F., Garcia, XdT., Cascella, G.L. and Salvatore, N. (2008) Surrogate assisted local search in PMSM drive design. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 27, (3), pp. 573-592 en
dc.identifier.issn 0332-1649
dc.identifier.uri http://hdl.handle.net/2086/6813
dc.description.abstract Purpose – This paper aims to propose a reliable local search algorithm having steepest descent pivot rule for computationally expensive optimization problems. In particular, an application to the design of Permanent Magnet Synchronous Motor (PMSM) drives is shown. Design/methodology/approach – A surrogate assisted Hooke-Jeeves algorithm (SAHJA) is proposed. The SAHJA is a local search algorithm with the structure of the Hooke-Jeeves algorithm, which employs a local surrogate model dynamically constructed during the exploratory move at each step of the optimization process. Findings – Several numerical experiments have been designed. These experiments are carried out both on the simulation model (off-line) and at the actual plant (on-line). Moreover, the off-line experiments have been considered in non-noisy and noisy cases. The numerical results show that use of the SAHJA leads to a saving in terms of computational cost without requiring any extra hardware components. Originality/value – The surrogate approach in the design of electric drives is novel. In addition, implementation of the proposed surrogate model allows the algorithm not only to reduce computational cost but also to filter noise caused by the sensors and measurement devices. Keywords – Magnetic devices, Optimization techniques, Numerical analysis Paper type Research paper en
dc.language.iso en en
dc.publisher Emerald Insight en
dc.subject magnetic devices en
dc.subject optimization techniques en
dc.subject numerical analysis en
dc.title Surrogate assisted local search in PMSM drive design en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1108/03321640810861043
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en


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