Neural Network Control of Non-Linear Full Vehicle Model Vibrations

Abstract

Vehicle suspension serves the basic function of isolating passengers and the chassis from the roughness of the road to provide a more comfortable ride. In other words, very important role of the suspension system is the ride control. Due to developments in the control technology, electronically controlled suspensions have gained more interest. These suspensions have active components controlled by a microprocessor. By using this arrangement, significant achievements in vehicle response can be carried out. Selection of the control method is also important during the design process. In this study, Neural Network (NN) controllers parallel to McPherson strut-type independent suspensions are used. The major advantages of this control method are its success, robust structure and the ability and adaptation of using these types of controllers on vehicles. To simplify models, a number of researchers assumed vehicle models to be linear. However, such models ignore non-linearities present in the system. By including non-linearities such as dry friction on dampers, the results become more realistic. During the last decade, many researchers applied some linear and non-linear control methods to vehicle models. Due to simplicity, quarter car models were mostly preferred. (Redfield & Karnopp, 1998) examined the optimal performance comparisons of variable component suspensions on a quarter car model. (Yue et al., 1989) also applied LQR and LQG controller to a quarter car model. (Stein & Ballo, 1991) designed a driver’s seat for off-road vehicles with active suspensions. Hac (Hac, 1992) applied optimal linear preview control on the active suspensions of a quarter car model. (Rakheja et al., 1994) added a passenger seat in their analysis. A passenger seat suspension system was described by a generalized two degrees of freedom model and with non-linearities such as shock absorber damping, linkage friction and bump stops. Since the quarter car model is insufficient to give information about the angular motions of a vehicle, some researchers used more complex models like half and full car models. These models give information about the pitch, roll and bounce motions of a vehicle body. (Crolla & Abdel Hady, 1991) compared some active suspension control laws on a full car model. Integrated or filtered white noise was taken as the road input. The same researchers applied linear optimal control law to a similar model in 1992. (Hrovat, 1993) compared the performances of active and passive suspension systems on quarter, half and full car models using linear quadratic optimal control. Source: Vibration Control, Book edited by: Dr. Mickael Lallart, ISBN 978-953-307-117-6, pp. 380, September 2010, Sciyo, Croatia, downloaded from SCIYO.COM