A novel genetic programming approach to the design of engine control systems for the voltage stabilisation of hybrid electric vehicle generator outputs

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Gladwin:2011:pimed,

• author = "D. Gladwin and Paul Stewart and Jill Stewart",
• title = "A novel genetic programming approach to the design of engine control systems for the voltage stabilisation of hybrid electric vehicle generator outputs",
• journal = "Proceedings of the Institute of Mechanical Engineers Part D - Automobile Engineering",
• year = "2011",
• volume = "225",
• number = "10",
• pages = "1334--1346",
• month = oct,
• keywords = "genetic algorithms, genetic programming, electronic and electrical engineering",
• ISSN = "0954-4070",
• DOI = "doi:10.1177/0954407011407414",
• size = "13 pages",
• publisher = "Institute of Mechanical Engineers",
• abstract = "This paper describes a Genetic Programming based automatic design methodology applied to the maintenance of a stable generated electrical output from a series-hybrid vehicle generator set. The generator set comprises a 3-phase AC generator whose output is subsequently rectified to DC.The engine/generator combination receives its control input via an electronically actuated throttle, whose control integration is made more complex due to the significant system time delay. This time delay problem is usually addressed by model predictive design methods, which add computational complexity and rely as a necessity on accurate system and delay models. In order to eliminate this reliance, and achieve stable operation with disturbance rejection, a controller is designed via a Genetic Programming framework implemented directly in Matlab, and particularly, Simulink. the principal objective is to obtain a relatively simple controller for the time-delay system which doesn{'}t rely on computationally expensive structures, yet retains inherent disturbance rejection properties. A methodology is presented to automatically design control systems directly upon the block libraries available in Simulink to automatically evolve robust control structures.",
• bibsource = "OAI-PMH server at eprints.lincoln.ac.uk",
• oai = "oai:eprints.lincoln.ac.uk:4352",
• type = "PeerReviewed",
• URL = "http://eprints.lincoln.ac.uk/4352/",
• notes = "http://www.uk.sagepub.com/journals/Journal202018",

}

Genetic Programming entries for Daniel Gladwin Paul Stewart Jill Stewart

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