The impact of suspension control on the controllability of the lateral vehicle dynamics

This paper aims to investigate uncertainties in railway vehicle suspension components and the implement of uncertainty quantification methods in railway vehicle dynamics. The sampling-based method represented by Latin Hypercube Sampling (LHS) and generalized polynomial chaos approaches including the stochastic Galerkin and Collocation methods (SGM and SCM) are employed to analyze the propagation of uncertainties from the parameters input in a vehicle-track mathematical model to the results of running dynamics. In order to illustrate the performance qualities of SGM, SCM and LHS, a stochastic wheel model with uncertainties of the stiffness and damping is firstly formulated to study the vertical displacement of wheel. Numerical results show that SCM, which can be easily implemented by means of the existing deterministic model, has explicit advantages over SGM and LHS in terms of the efficiency and accuracy. Furthermore, a simplified stochastic bogie model with three random suspension parameters is also established by means of SCM and LHS to analyze the critical speed, which is affected obviously by the parametric uncertainties. Finally, a stochastic vertical vehicle-track coupled model with parametric uncertainties is built comprehensively on the basis of SCM, by which the impact behavior of wheel-rail interaction under a rail defect is investigated and the dynamic response of vehicles under the track irregularity is explored in terms of the Sperling index. It concludes that the uncertainties of parameters have a significant influence on P2 force and Sperling index from the view of the running quality.

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NONLINEAR OBSERVER DESIGN FOR LATERAL VEHICLE DYNAMICS

IFAC Proceedings Volumes ◽

10.3182/20050703-6-cz-1902.00166 ◽

2005 ◽

Vol 38 (1) ◽

pp. 988-993 ◽

Cited By ~ 5

Author(s):

Anne von Vietinghoff ◽

Marcus Hiemer ◽

Uwe Kiencke

Keyword(s):

Vehicle Dynamics ◽

Observer Design ◽

Nonlinear Observer ◽

Lateral Vehicle Dynamics

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Motion Simulation Experiments for Driver Behavior and Road Vehicle Dynamics

Journal of Computing and Information Science in Engineering ◽

10.1115/1.3617437 ◽

2011 ◽

Vol 11 (4) ◽

Author(s):

Kemper Lewis ◽

Kevin Hulme ◽

Edward Kasprzak ◽

Deborah Moore-Russo ◽

Gregory Fabiano

Keyword(s):

Vehicle Dynamics ◽

Large Scale ◽

Driving Simulation ◽

Vehicle Design ◽

Simulation Environment ◽

Learning Mechanisms ◽

Road Vehicle ◽

Wide Range ◽

Traffic Systems ◽

The Impact

This paper discusses the design and development of a motion-based driving simulation and its integration into driving simulation research. The integration of the simulation environment into a road vehicle dynamics curriculum is also presented. The simulation environment provides an immersive experience to conduct a wide range of research on driving behavior, vehicle design and intelligent traffic systems. From an education perspective, the environment is designed to promote hands-on student participation in real-world engineering experiences that enhance conventional learning mechanisms for road vehicle dynamics and engineering systems analysis. The paper assesses the impact of the environment on student learning objectives in an upper level vehicle dynamics course and presents results from research involving teenage drivers. The paper presents an integrated framework for the use of real-time simulation and large-scale visualization to both study driving behaviors and to discover the impact that design decisions have on vehicle design using a realistic simulated driving interface.

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