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TECHNICAL PAPERS

Hybrid Genetic Algorithm: A Robust Parameter Estimation Technique and its Application to Heavy Duty Vehicles

[+] Author and Article Information
Jie Xiao

 United Technologies Research Center, 411 Silver Lane, MS 129-17, East Hartford, CT 06067

Bohdan Kulakowski

 Pennsylvania State University, 201 Transportation Research Building, University Park, PA 16802

J. Dyn. Sys., Meas., Control 128(3), 523-531 (Sep 12, 2005) (9 pages) doi:10.1115/1.2229255 History: Received November 24, 2003; Revised September 12, 2005

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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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Block diagram of the hybrid Genetic Algorithm

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Figure 2

The vehicle dynamic model

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Figure 3

Sensitivity measures of the bounce rate żm, roll rate ϕ̇, and pitch rate ψ̇ of the sprung mass with respect to the parameters to be estimated

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Figure 4

Convergence history of the GA-based parameter estimation

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Figure 5

Convergence history of the gradient-based MLE

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Figure 6

Parameter estimation errors

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Figure 7

A comparison of dynamic responses

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Figure 8

Convergence history of pure GA-based estimation

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Figure 9

Normalized standard variances of estimates versus the Cramer-Rao lower bounds

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Figure 10

The parameter estimation diagram

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Figure 11

Sampled signals from accelerometers at the front suspension corresponding to different sections of the test track

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Figure 12

A comparison in the time domain

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Figure 13

A comparison in the frequency domain

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