The Open Mechanical Engineering Journal, 2009, 3, 72-79
Research on Suspension System Based on Genetic Algorithm and Neural Network Control Chuan-Yin Tang and Li-Xin Guo*
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.
INTRODUCTION Suspension is the term given to the system of springs, shock absorbers and linkages that connects a vehicle to its wheels. Suspension systems can not only contribute to the car's handling and braking for good active safety and driving pleasure, but also keep vehicle occupants comfortable and reasonably well isolated from road noise, bumps, and vibrations. The suspension also protects the vehicle itself and any cargo or luggage from damage and wear. The ride quality of a vehicle is significantly influenced by its suspension system, the road surface roughness, and the speed of vehicle. A vehicle designer can do little to improve road surface roughness, so designing a good suspension system with good vibration performance under different road conditions become s a prevailing philosophy in the automobile industry. Passive suspension systems use conventional dampers to absorb vibration energy, the dampers and stiffness coefficients are constant. The active suspension system use extra power to provide a response-dependent damper, which is capable of producing an improved ride comfort. Over the years, both passive and active suspension systems have been proposed to optimize a vehicle’s ride quality. O. GD used genetic algorithm to obtain the optimal set and suspension design (O. GD. IJIE 2007) , Sun L, Cai XM, Yang J. got the minimum dynamic pavement load through the genetic algorithm (Sun L, Cai XM, Yang J. JSV 2007) . A simplified algorithm for the evaluation of a small car suspension model can be found in the papers of T.G. Chondros (T.G. Chondros, S. Michalitsis, S. Panteliou and A.D. Dimarogonas 1994)  and (T.G. Chondros, P.A. Belokas, K. Vamvakeros and A.D. Dimarogonas 1997) , Furthermore, a more detailed model for heavy vehicles suspension systems are given in the paper (Chondros T. G.,
Michalos G, Michaelides P, Fainekos E 2007) . The stateof-the-art review on neural networks in automotive applications can be found in the papers (J.T. Papadimitropoulos, T.G. Chondros, S.D. Panteliou, B. Carlsson, S. Kalogirou and A.D. Dimarogonas 1999)  and (S. Kalogirou, T.G. Chondros, A.D. Dimarogonas 2000) . A variety of research projects and publications deal with different types of active suspension systems have been discussed (Yeh, E.C. and Tsao, Y.J. 1994) . Different vehicle dynamic models have been adopted according to different study purposes during research. A two degree-of freedom quarter body of vehicle suspension system model had been widely applied in vehicle suspension control research, it can indicate the vehicle body vertical movement, but not include the pitching movement of the vehicle body. Although a seven degree-of -freedom whole-body of vehicle suspension system model can describe...
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