Evolutionary Optimization on Turning Linkage of Automobile in Simulated Annealing

Article Preview

Abstract:

The main function of turning linkage of automobile is to realize the ideal relations of turn angle of the internal and external wheels when vehicles steering. At present the main methods on design computing and verifying turning linkage have still been the planar graphing and analysis method, therefore it is very important to adopt optimization methods to design the steering linkage. Being satisfied with the Ackerman theory steering characteristics and boundary constraints, considering the ideal relationship of steering angles between external and internal wheels in steering linkage to ensure motion accuracy of automobile, optimization model of turning linkage is established. Then the objective function with penalty terms is built by penalty strategy with addition type, so the constrained optimization is transformed into the unconstrained optimization. The simulated annealing algorithm is adopted to optimize turning linkage of automobile, so that optimization process was simplified and the global optimal solution is ensured reliably.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

317-321

Citation:

Online since:

October 2010

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2010 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Du Minggang, Yan Qingdong, Zhou Guangming, Research on Numerical Simulation and Experiment of Hybrid Steering System for Vehicles, Drive System Technique, Vol. 5(2005), p.159.

Google Scholar

[2] Man Huitin, Theory and applying Simulated Annealing Algorithm. Peking University Press, Beijing(2004).

Google Scholar

[3] T.T.H. Ng, G.S.B. Leng, Engineering Applications of Artificial Intelligence, Department of Mechanical Engineering, National University of Singapore. Singapore(2005), p.439.

Google Scholar

[4] Wu Xiaojian, Liao Linqing, Xie Ming, Mathematic Model for Rack and Pinion Steering Mechanism Space, Journal of Chongqing Institute of Technology(Natural Science), Vol. 3(2009), p.139.

Google Scholar

[5] Zhao Shian, Huang Ganji, Design and Study of Particle Swarm Optimization with Simulated Annealing, Journal of Baise University, Vol. 6(2006), p.124.

Google Scholar

[6] Gao Shang , Optimization Computing Based on the Genetic Algorithm Optimization Toolbox in Matlab, Microcomputth Applications, Vol. 8(2002), p.28.

Google Scholar