Simulation tools have been widely used to complement experimentation for suspension design in the automotive industry not only for reducing the development time, but also to allow the optimization of the vehicle performance. Both a test method and a simulation tool are presented for the analysis of Noise-Vibration-Harshness (NVH) performances of road vehicles suspension systems. A single suspension (corner) has been positioned on a rotating drum (2.6 m diameter) installed in the Laboratory for the Safety of Transport of the Politecnico di Milano. The suspension system is excited as the wheel passes over different cleats fixed to the working surface of the drum. The forces and the moments acting at the suspension-chassis joints are measured up to 250 Hz by means of five six-axis load cells. A mathematical representation that can accurately reflect tyre dynamic behaviour while passing over different cleats is fundamental for evaluating the suspension system quality (NVH) and for developing new suspension design and control strategies. Since the phenomenon is highly non-linear, it is rather difficult to predict the actual performance by using a physical model. However universal "black-box" models can be successfully used in the identification and control of non-linear systems. The paper deals with the simulation of the tyre/suspension dynamics by using Recurrent Neural Networks (RNN). RNN are derived from the Multi Layer Feed-forward Neural Networks (MLFNN), by adding feedback connections between output and input layers. The Neural Network (NN) has been trained with the experimental data obtained in the laboratory. The results obtained from the NN demonstrate very good agreement with the experimental results over a wide range of operation conditions. The NN model can be effectively applied as a part of vehicle system model to accurately predict elastic bushings and tyre dynamics behaviour.

Volume Subject Area:
Design Engineering
Topics:
Computer simulation, Tires, Artificial neural networks, Simulation, Suspension systems, Design, Dynamics (Mechanics), Vehicles, Automotive industry, Bushings, Corners (Structural elements), Feedback, Feedforward control, Motor vehicles, Noise (Sound), Nonlinear systems, Optimization, Safety, Stress, Vibration, Wheels
1.
Bernard J. Challen (editor), Vehicle Noise and Vibration: Recent Engineering Developments, Society of Automotive Engineers, 2004.
2.
Haga
H.
, “
Evaluation of tyre models for durability loads prediction using a suspension-on-a-drum environment
”,
Vehicle System Dynamics
, Vol.
43
, pp:
281
–
296
, Supplement,
2005
.
3.
Zegelaar P.W.A., “The dynamic Response of Tyres to Brake Torque Variations and Road Unevenness”, PhD Thesis, Delft University of Technology, The Netherlands, 1998.
4.
Pacejka H.B., Tyre and Vehicle Dynamics, Butterworth-Heinemann, 2006.
5.
Higuchi A., “Transient Response of Tyres to Large Wheel Slip and Camber”, PhD Thesis, Delft University of Technology, The Netherlands, 1998.
6.
Maurice J.P., “Short Wavelength and Dynamic Tyre Behaviour under Lateral and Combined Slip Conditions”, PhD Thesis, Delft University of Technology, The Netherlands, 1999.
7.
Guan, D., Fan, C. “A dynamic tyre model of vertical performance rolling over cleats”. Proc. of the TMVDA Int. conference, Wien, 2004.
8.
Troulis
M.
,
Gnadler
R.
and
Unrau
H. J.
, “
The Transfer Characteristics of Wheel Suspensions for passenger Cars in the Comfort-Relevant Frequency Range
”,
ATZ worldwide 4/2004
Volume
106
, pp:
13
–
17
.
9.
Troulis M., “U¨bertragungsverhalten von Radaufha¨ngungen fu¨r Personenwagen im komfortrelevanten Frequenzbereich. Universita¨t Karlsruhe”, Dissertation, Shaker Verlag, 2002, ISBN3-8322-0850-X.
10.
Popov
A. A.
,
Cole
D. J.
,
Winkler
C. B.
and
Cebon
D.
, “
Laboratory measurement of rolling resistance in truck tyres under dynamic vertical load
”,
Proc. Instn Mech. Engrs Part D: J. Automobile Engineering
, Vol.
217
, pp:
1071
–
1079
,
2003
.
11.
Allison
D. J.
, and
Sharp
R. S.
, “
On the low frequency in-plane forced vibrations of pneumatic tyre / wheel / suspension assemblies
”,
Vehicle System Dynamics
, Vol.
27
, pp:
151
–
162
, Supplement,
1997
.
12.
http://www.ftire.com, 2006
13.
http://www.lmsintl.com, 2006
14.
Patel A., Dunne J. F., “NARX neural network modelling of hydraulic suspension dampers for steady-state and variable temperature operation”, Vehicle systems dynamics, Swets & Zeitlinger, 2003.
15.
Duym S., Reybrouck K., “Physical characterization of nonlinear shock absorber dynamics”, Eur. J. Mech. Eng., 1998.
16.
Mills, V., e altri, “Fourier series neural network analysis of a steer-by-wire system”, International Mechanical Engineering Congress and Exposition, ASME, New Orleans, 2002.
17.
Cao M., et Al., “Application of neural network friction component model in automotive drivetrain simulations”, International Mechanical Engineering Congress and Exposition, ASME, New York, 2001.
18.
Wang K. W., Parvataneni V., “A neural network-based clutch dynamics model”, Penn State Report to Ford Motor Company, 1997.
19.
Cao, M., et Al., “Automatic shift calibration based on parallel-modulated neural network (PMNN) friction component model”, International Mechanical Engineering Congress and Exposition, ASME, Anaheim, 2004.
20.
Yim Y. O., Oh S., “Modelling of vehicle dynamics from real vehicle measurements using a neural network with two-stage hybrid learning for accurate long-term prediction”, Transactions on vehicular technology, IEEE, 2004.
21.
Mastinu G., Pennati M., and Gobbi M., “Design and Construction of a Test Rig for Assessing Tyre Characteristics at Rollover”, SAE Transactions: Journal of Passenger Cars -Mechanical Systems, pp: 2023–2040, 2003.
22.
Gobbi M., Mastinu G., and Giorgetta F., “Sensors for measuring forces and moments with application to ground vehicle design and engineering”, Proc. of the ASME IMECE, Orlando, 2005.
23.
Mastinu G., Gobbi M., “Device and method for measuring forces and moments”. International Patent WO2005/015146 A1, 2005.
24.
http://www.vicon.com, ViconPEAK Optical Tracking Systems, 5/2005.
25.
Gobbi
M.
,
Haque
I.
,
Papalambros
P.
,
Mastinu
G.
 “
Optimization and integration of ground vehicle systems
”
Vehicle System Dynamics
Vol.
43
, No.
6–7
, June–July
2005
, p.
437
–
453
.
26.
Gobbi
M.
,
Levi
F.
,
Mastinu
G.
, “
Multi Objective Robust Design of the Suspension System of Road Vehicles
”,
Vehicle System Dynamics Supplement
41
:
537
–
546
,
2004
.
27.
Gobbi, M., Levi, F., Mastinu, G., “Optimal and Robust Design of a Road Vehicle Suspension System”, to appear on the Journal of Sound and Vibration, 2005.
28.
Gobbi M., Mastinu G., “Symbolic description and optimisation of the dynamic behaviour of vehicles running on rough road”, Journal of Sound and Vibration, Vol. 245, No. 3, August, 2001.
29.
Gobbi, M., Mastinu, G., “Symbolical multi-objective optimisation of the dynamic behaviour of actively suspended road vehicles”, Int. Journal of Vehicle Design, Vol. 28, Nos. 1/2/3, 2002.
30.
Ljung, L., “System identification”, Prentice Hall, Upper Saddle River, New Jersey, 1987.
31.
Bittanti S., “Identificazione dei modelli e controllo adattivo”, Pitagora Editrice Bologna.
32.
Haykin S., “Neural networks, a comprehensive foundation”, Prentice Hall, Second edition, 1999.
33.
Su S., Yang F., “On the dynamical modeling with neural fuzzy networks”, Transactions on neural networks, IEEE, 2002.
34.
Guarneri P., Rocca G., Gobbi M., “A neural network based model for the dynamic simulation of the tyre/suspension system while traversing road irregularities”, submitted for publication, 2006.
35.
Hagan M. T., Demuth H. B., Beale M. H., “Neural Network Design”, Boston, MA: PWS Publishing, 1996.
36.
Hagan
M. T.
,
Menhaj
M.
, “
Training feed forward networks with the Marquardt algorithm
”,
IEEE Transactions on Neural Networks
, vol.
5
, no.
6
, pp.
989
–
993
,
1994
.
37.
MacKay
D. J. C.
, “
Bayesian interpolation
”,
Neural Computation
, vol.
4
, no.
3
, pp.
415
–
447
,
1992
.
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