Data-based modeling of vehicle collision by LPV-ARMAX model approach

Vehicle crash are considered to be events with high complexity from the mathematical points of view. The high experiment cost and huge time-consumption make the establishment of a mathematical model of vehicle crash which can simplify the analysis process in great demand. In this work, we present the application of LPV-ARMAX model to simulate the car-to-pole collision with different initial impact velocities. The parameters of the LPV-ARMAX are assumed to be functions of the initial impact velocities. Instead of establishing a set of LTI models for vehicle crashes with various impact velocities, the LPV-ARMAX model is comparatively simple and applicable to predict the responses of new collision situations different from those used for identification. The comparison between the predicted response and the real test data is conducted, which shows the high fidelity of the LPV-ARMAX model.

[1]  A. Várkonyi-Kóczy,et al.  Intelligent Methods for Car Deformation Modeling and Crash Speed Estimation , 2004 .

[2]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[3]  Bo-Suk Yang,et al.  A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting , 2010, Expert Syst. Appl..

[4]  S. J. Hu,et al.  Data-based approach in modeling automobile crash , 1995 .

[5]  Hamid Reza Karimi,et al.  A fuzzy logic approach to modeling a vehicle crash test , 2012 .

[6]  Hamid Reza Karimi,et al.  Signal Analysis and Performance Evaluation of a Vehicle Crash Test with a Fixed Safety Barrier Based on Haar Wavelets , 2011, Int. J. Wavelets Multiresolution Inf. Process..

[7]  Hamid Reza Karimi,et al.  Signal reconstruction, modeling and simulation of a vehicle full-scale crash test based on Morlet wavelets , 2012, Neurocomputing.

[8]  Matej Borovinšek,et al.  Simulation of crash tests for high containment levels of road safety barriers , 2007 .

[9]  Ahmed Elmarakbi,et al.  Crash analysis and modeling of two vehicles in frontal collisions using two types of smart front-end structures: an analytical approach using IHBM , 2006 .

[10]  John McFadden,et al.  Application of Artificial Neural Networks to Predict Speeds on Two-Lane Rural Highways , 2001 .

[11]  Gustavo Belforte,et al.  LPV approximation of distributed parameter systems in environmental modelling , 2005, Environ. Model. Softw..

[12]  Ching-Hsue Cheng,et al.  A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting , 2010, Inf. Sci..

[13]  L. Faes,et al.  Linear and nonlinear parametric model identification to assess granger causality in short-term cardiovascular interactions , 2008, 2008 Computers in Cardiology.

[14]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[15]  Z Moumni,et al.  Simplified modelling of vehicle frontal crashworthiness using a modal approach , 2004 .

[16]  Laura Giarré,et al.  NARX models of an industrial power plant gas turbine , 2005, IEEE Transactions on Control Systems Technology.

[17]  Hamid Reza Karimi,et al.  Comparative analysis of vehicle to pole collision models established using analytical methods and neural networks , 2010 .

[18]  H. Zohm,et al.  Autoregressive moving average model for analyzing edge localized mode time series on Axially Symmetric Divertor Experiment (ASDEX) Upgrade tokamak , 2004 .

[19]  Noureddine Zerhouni,et al.  Defining and applying prediction performance metrics on a recurrent NARX time series model , 2010, Neurocomputing.

[20]  K. Jeong,et al.  Non-linear autoregressive modelling by Temporal Recurrent Neural Networks for the prediction of freshwater phytoplankton dynamics , 2008 .

[21]  Eindhoven,et al.  LPV Modeling of Vehicle Occupants , 2008 .