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Reseach Article

Online Wavelet Denoising for a Quarter Car Model

by Seda Postalcıoğlu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 31
Year of Publication: 2018
Authors: Seda Postalcıoğlu
10.5120/ijca2018916612

Seda Postalcıoğlu . Online Wavelet Denoising for a Quarter Car Model. International Journal of Computer Applications. 179, 31 ( Apr 2018), 25-28. DOI=10.5120/ijca2018916612

@article{ 10.5120/ijca2018916612,
author = { Seda Postalcıoğlu },
title = { Online Wavelet Denoising for a Quarter Car Model },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 179 },
number = { 31 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 25-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number31/29195-2018916612/ },
doi = { 10.5120/ijca2018916612 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:57:08.272147+05:30
%A Seda Postalcıoğlu
%T Online Wavelet Denoising for a Quarter Car Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 31
%P 25-28
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Noise impact cannot be ignored in control system. In online systems when the received data by sensor contains noise, may cause to a problem. Linear position sensor can be widely used to control of body motion in different types of suspension system. Sensor’s resolution, accuracy and stability depend on its electronics design. For this purpose, in this paper an online wavelet denoising has been studied for a quarter car model. Vibrations due to the unit step input are controlled with PID controller. If the sensor contains noise, controller performance will be poor. Online wavelet denoising is used to eliminate the noise. Simulation results show that when the system has online wavelet denoising, controller gives better results and system is not affected by the noise. As a result, this type of control strategy can be applied to the semi-active suspension systems to improve driver comfort.

References
  1. Lin C.M.,Chen C.H.,Car-Following Control Using Recurrent Cerebellar Model Articulation Controller, IEEE transactions on vehicular technology, vol. 56, no. 6, pp:3660-3673. 2007.
  2. Yang R, Ren M, Wavelet denoising using principal component analysis. Expert Systems with Applications 38, 1073-1076,2011.
  3. Qibing J, Sajid K, General theory on online wavelets denoising based on moving window. International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 157 – 161, 9-11 May 2013.
  4. Qifeng F., Guoqing C. and Zibo S., Application of Wavelet De-Noising Method in vibration signal analysis of Elevator Car,13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAl), pp:610-614. August 19-22, 2016 at Sofitel Xian on Renmin Square, Xian, China.
  5. Chetan R.G, Both-Rusu R., Dulf E.-H., Festila C., Physical Model of a Quarter-Car Active Suspension System, Carpathian Control Conference (ICCC), 2017 18th International, pp:517-520,28-31 May 2017, Romania.
  6. Mahajan B. D., Divekar A.A., Modeling and System Identification of a Quarter car Suspension using Simulink, IEEE International Conference On Recent Trends In Electronics Information Communication Technology,pp:180-183. May 20-21, 2016, India
  7. Vidya V., Dharmana M.M., Model Reference Based Intelligent Control of an Active Suspension System for Vehicles, International Conference on circuits Power and Computing Technologies [ICCPCT],pp:1-5. 20-21 April 2017, India
  8. Ericksen E O and Faramarz G., A Magneto-Rheological Fluid Shock Absorber for an Off-Road Motorcycle,. International Journal of Vehicle Design, 33(1): 139-152, 2003.
  9. Qazi A J, Khan A, Khan M T, Noor S, A Parametric Study on Performance of Semi-Active Suspension System with Variable Damping Coefficient Limit, AASRI Procedia 4, 154 – 159, 2013.
  10. Sánchez E A, A quarter-car suspension system: car body mass estimator and sliding mode control, Procedia Technology 7, 208 – 214 2013.
  11. Fang Z, Shu W, Du D, Xiang B, He Q, He K, Semi-active Suspension of a Full-vehicle Model based on Double-loop Control, Procedia Engineering 16, 428 – 437, 2011.
  12. Krauze, P. , Kasprzyk, J. Neural Network Based LQ Control of a Semiactive Quarter-Car Model, 18th International Conference on Methods and Models in Automation and Robotics (MMAR), 189 – 194, 26-29 Aug. 2013.
  13. Wagner U V, On non-linear stochastic dynamics of quarter car models, International journal of non-linear mechanics 39, 753-765, 2004.
  14. Verros G, Natsiavas S, Papadimitriou C, Design Optimization of quarter car models with passive and semi active suspensions under random road excitation. Journal of Vibration and Control 11,581-606, 2005.
  15. Gobbi M, Mastinu G, Analytical description and optimization of the dynamic behaviour of passively suspended road vehicles. Journal of sound and vibration 245(3), 457-481, 2001.
  16. Gobbi, M., Levi F., Mastinu G., "Multi-objective stochastic optimisation of the suspension system of road vehicles", Journal of Sound and Vibration 298 pp:1055–1072, 2006.
  17. Shi X, Zhao X, Hui F, Yang L, Processing of Hydraulic pressure sensor signal based on wavelet analysis. 2012 International conference on applied physics and industrial engineering. International Conference on Applied Physics and Industrial Engineering, Physics Procedia 24, 2143-2150, 2012.
  18. Rui X, Ke M, Feng Q, Zhen Lei W, Online wavelet denoising via a moving window. Acta Automatica Sinica Vol.33, No:9,897-901, 2007.
  19. Liu Z. , Mi Y. , Mao Y., An Improved Real-time Denoising Method Based on Lifting Wavelet Transform Measurement Science Review, Volume 14, Issue 3, Pages 152–159,2014.
  20. Shu-Jen Steven Tsai, Power Transformer Partial Discharge (PD) Acoustic Signal Detection using Fiber Sensors and Wavelet Analysis, Modeling and Simulation, Master of Science, the faculty of the Virginia Polytechnic Institute and State University,2002.
  21. Kamath C., Baldwin C.H., Fodor I.K., Tang N.A., "Design and implementation of a parallel object-oriented image processing toolkit," Proc. SPIE 4118, Parallel and Distributed Methods for Image Processing IV, 9 October 2000.
  22. Postalcıoğlu, S., Erkan, K., Doğru Bolat E., “Comparison of Kalman Filter and Wavelet Filter for Denoising”, Proc. of 2005 Int. Conference on Neural Networks and Brain, ICNNB’05, vol 1-3, 951-954, China,2005.
  23. Postalcıoğlu, S., Erkan, K., Doğru Bolat E., “Discrete Wavelet Analysis Based Fault Detection”, WSEAS Transactions on Systems, Issue 10, Volume 5, 2391-2397, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Wavelet denoising PID control vehicle safety.