REVIEW ARTICLE


Research on Suspension System Based on Genetic Algorithm and Neural Network Control



Chuan-Yin Tang , Li-Xin Guo
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China.


Tang et al.; Licensee Bentham Open

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China.


Abstract

In this paper, a five degree of freedom half body vehicle suspension system is developed and the road roughness intensity is modeled as a filtered white noise stochastic process. Genetic algorithm and neural network control are used to control the suspension system. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of not only sprung mass acceleration, pitching acceleration, suspension travel and dynamic load, but also the passenger acceleration. With the aid of software Matlab/Simulink, the simulation model is achieved. Simulation results demonstrate that the proposed active suspension system proves to be effective in the ride comfort and drive stability enhancement of the suspension system. A mechanical dynamic model of the five degree of freedom half body of vehicle suspension system is also simulated and analyzed by using software Adams.

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