Toward a unified and automated design methodology for multi-domain dynamic systems using bond graphs and genetic programming

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@Article{seo:2003:M,
  author =       "Kisung Seo and Zhun Fan and Jianjun Hu and 
                 Erik D. Goodman and Ronald C. Rosenberg",
  title =        "Toward a unified and automated design methodology for
                 multi-domain dynamic systems using bond graphs and
                 genetic programming",
  journal =      "Mechatronics",
  year =         "2003",
  volume =       "13",
  number =       "8-9",
  pages =        "851--885",
  month =        oct,
  note =         "Computational Intelligence in Mechatronic Systems",
  keywords =     "genetic algorithms, genetic programming, Automated
                 design, Bond graph, Multi-domain dynamic system",
  URL =          "http://www-rcf.usc.edu/~jianjunh/paper/mechatronics_gpbg.pdf",
  ISSN =         "0957-4158",
  DOI =          "doi:10.1016/S0957-4158(03)00006-0",
  URL =          "http://www.sciencedirect.com/science/article/B6V43-485XGFN-1/2/54359d4201bcd9935e6dbc231bbc7334",
  abstract =     "This paper suggests a unified and automated design
                 methodology for synthesising designs for multi-domain
                 systems, such as mechatronic systems. A multi-domain
                 dynamic system includes a mixture of electrical,
                 mechanical, hydraulic, pneumatic, and/or thermal
                 components, making it difficult use a single design
                 tool to design a system to meet specified performance
                 goals. The multi-domain design approach is not only
                 efficient for mixed-domain problems, but is also useful
                 for addressing separate single-domain design problems
                 with a single tool. Bond graphs (BGs) are domain
                 independent, allow free composition, and are efficient
                 for classification and analysis of models, allowing
                 rapid determination of various types of acceptability
                 or feasibility of candidate designs. This can sharply
                 reduce the time needed for analysis of designs that are
                 infeasible or otherwise unattractive. Genetic
                 programming is well recognised as a powerful tool for
                 open-ended search. The combination of these two
                 powerful methods is therefore an appropriate target for
                 a better system for synthesis of complex multi-domain
                 systems. The approach described here will evolve new
                 designs (represented as BGs) with ever-improving
                 performance, in an iterative loop of synthesis,
                 analysis, and feedback to the synthesis process. The
                 suggested design methodology has been applied here to
                 three design examples. The first is a
                 domain-independent eigenvalue placement design problem
                 that is tested for some sample target sets of
                 eigenvalues. The second is in the electrical
                 domain--design of analog filters to achieve specified
                 performance over a given frequency range. The third is
                 in the electromechanical domain--redesign of a printer
                 drive system to obtain desirable steady-state position
                 of a rotational load.",
}

Genetic Programming entries for Kisung Seo Zhun Fan Jianjun Hu Erik Goodman Ronald C Rosenberg

Citations