In this paper, hybrid parameter estimation technique is developed to improve computational efficiency and accuracy of pure GA-based estimation. The proposed strategy integrates a GA and the Maximum Likelihood Estimation. Choices of input signals and estimation criterion are discussed involving an extensive sensitivity analysis. Experiment-related aspects, such as the imperfection of data acquisition, are also considered. Computer simulation results reveal that the hybrid parameter estimation method proposed in this study is very efficient and clearly outperforms conventional techniques and pure GAs in accuracy, efficiency, as well as robustness with respect to the initial guesses and measurement uncertainty. Primary experimental validation is also implemented, including the interpretation of field test data, as well as analysis of errors associated with aspects of experiment design.
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September 2006
Technical Papers
Hybrid Genetic Algorithm: A Robust Parameter Estimation Technique and its Application to Heavy Duty Vehicles
Jie Xiao,
Jie Xiao
Senior Research Engineer
United Technologies Research Center
, 411 Silver Lane, MS 129-17, East Hartford, CT 06067
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Bohdan Kulakowski
Bohdan Kulakowski
Professor of Mechanical Engineering
Pennsylvania State University
, 201 Transportation Research Building, University Park, PA 16802
Search for other works by this author on:
Jie Xiao
Senior Research Engineer
United Technologies Research Center
, 411 Silver Lane, MS 129-17, East Hartford, CT 06067
Bohdan Kulakowski
Professor of Mechanical Engineering
Pennsylvania State University
, 201 Transportation Research Building, University Park, PA 16802J. Dyn. Sys., Meas., Control. Sep 2006, 128(3): 523-531 (9 pages)
Published Online: September 12, 2005
Article history
Received:
November 24, 2003
Revised:
September 12, 2005
Citation
Xiao, J., and Kulakowski, B. (September 12, 2005). "Hybrid Genetic Algorithm: A Robust Parameter Estimation Technique and its Application to Heavy Duty Vehicles." ASME. J. Dyn. Sys., Meas., Control. September 2006; 128(3): 523–531. https://doi.org/10.1115/1.2229255
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