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The mathematical modeling of most physical systems, such as aerospace systems, heat processes, telecommunication systems, transmission lines and chemical reactors, results in complex high order models. The complexity of the models imposes a lot of difficulties in analysis, simulation and control designs. Several analytical model reduction techniques have been proposed in literature over the past few decades to reduce these difficulties. However, most of the optimal techniques follow computationally demanding, time consuming, iterative procedures that usually result in non-robustly stable models with poor frequency response resemblance to the original high order model in some frequency ranges. Genetic Algorithm (GA) has proved to be an excellent optimization tool in the past few years. Therefore, the aim of this paper will be to use GA to solve H2 and H∞ norm model reduction problems, and help obtain globally optimized nominal models. 相似文献
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