Automated Design Methodology for Mechatronic Systems Using Bond Graphs and Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{GARAGe02-01-01,
-
author = "Erik D. Goodman and Kisung Seo and
Ronald C. Rosenberg and Zhun Fan and Jianjun Hu and Baihai Zhang",
-
title = "Automated Design Methodology for Mechatronic Systems
Using Bond Graphs and Genetic Programming",
-
booktitle = "Proceedings 2002 NSF Design, Service and Manufacturing
Grantees and Research Conference",
-
year = "2002",
-
pages = "206--221",
-
address = "San Juan, Puerto Rico",
-
month = jan,
-
organization = "National Science Foundation",
-
publisher = "National Science Foundation",
-
keywords = "genetic algorithms, genetic programming",
-
URL = "http://garage.cse.msu.edu/papers/GARAGe02-01-01.pdf",
-
size = "16 pages",
-
abstract = "We suggest an automated design methodology for
synthesising designs for multi-domain systems, such as
mechatronic systems. The domain of mechatronic systems
includes mixtures of, for example, electrical,
mechanical, hydraulic, pneumatic, and thermal
components, making it difficult to design a system to
meet specified performance goals with a single design
tool. 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 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 bond graphs) 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 two design examples. One is domain
independent, an eigenvalues-placement design problem
which is tested for some sample target sets of
eigenvalues. The other is in the electrical domain --
namely, design of analog filters to achieve specified
performance over a given frequency range.",
-
notes = "https://www.nsf.gov/awardsearch/showAward?AWD_ID=0101000",
- }
Genetic Programming entries for
Erik Goodman
Kisung Seo
Ronald C Rosenberg
Zhun Fan
Jianjun Hu
Baihai Zhang
Citations