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.
Skip Nav Destination
Article navigation
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
Search for other works by this author on:
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
Download citation file:
Get Email Alerts
Cited By
Offset-Free Koopman Model Predictive Control of Thermal Comfort Regulation for A VRF-DOAS Combined System
J. Dyn. Sys., Meas., Control
Rejection of Sinusoidal Disturbances With Unknown Slowly Time-Varying Frequencies for Linear Time-Varying Systems
J. Dyn. Sys., Meas., Control (July 2024)
Using Control Barrier Functions to Incorporate Observability: Application to Range-Based Target Tracking
J. Dyn. Sys., Meas., Control (July 2024)
Gas Path Fault Diagnosis of Turboshaft Engine Based on Novel Transfer Learning Methods
J. Dyn. Sys., Meas., Control (May 2024)
Related Articles
A Co-Evolutionary Approach for Design Optimization via Ensembles of Surrogates With Application to Vehicle Crashworthiness
J. Mech. Des (January,2012)
Mechanical Fault Detection Based on the Wavelet De-Noising Technique
J. Vib. Acoust (January,2004)
Assessment of Multiobjective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design
J. Eng. Gas Turbines Power (May,2010)
The Impact of Data Accuracy on the POT Estimates of Long Return Period Design Values
J. Offshore Mech. Arct. Eng (February,2002)
Related Proceedings Papers
Related Chapters
A Method of Carrier Synchronization Based on Exit for LDPC Encoded Systems
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
Presenting a Channel Estimation Method with Considering the Carrier Frequency Offset Based on Comparative Methods in MIMO-OFDM Systems
International Conference on Computer Technology and Development, 3rd (ICCTD 2011)
Design of Experiments for Model Development and Validation
Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments