Email this article 
Modified Grey Fuzzy Logic Controller for Vehicle Suspension System
(*) Corresponding author
DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Abstract
This paper presents a Modified Grey Fuzzy Logic controller for enhancing the ride comfort and road holding ability of Vehicle Suspension System (VSS) simultaneously. Grey Prediction algorithm is used to pre-compensate the error. The prediction error in Grey Model (GM) is minimized by changing the initial condition and optimizing the weight factor in the data matrix of grey prediction algorithm. The aim of this paper is to design two Traditional Fuzzy Logic Controllers (TFLC), one for minimizing the sprung mass displacement error and the other for tyre deflection error. TFLC uses the grey model to design Grey Fuzzy Logic Controller based VSS. Particle Swarm Optimization (PSO) is used to optimize the weighting factor of grey model. The proposed controller is simulated for a Quarter Car model of VSS. Simulation results show that the proposed controller enhances both ride comfort and road holding ability simultaneously
Copyright © 2014 Praise Worthy Prize - All rights reserved.
Keywords
References
Alexandru.C and Alexandru.P, Control Strategy for an Active Suspension System, World Academy of Science, Engineering and Technology, Issue 55, pp. 126, July 2011.
Rajeswari.K and Lakshmi.P, Control of Active Suspension using Fuzzy Logic, International Conference on Modeling and Simulation, CTICMOS’07 CIT, Coimbatore, pp.993-999, 27-29th August 2007.
Jeen Lin, Ruey-Jing Lian, DSP-based self-organising fuzzy controller for active suspension systems, Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, Vol. 46( Issue 12):1123-1139, 2008.
Soleymani.M, Montazeri-Gh.M, Amiryan.R, Adapitve Fuzzy Controller for Vehicle Active Suspension System based on traffic conditions, Scientia Iranica, Vol.19 (Issue 3): 443-453, June 2012.
Juing-Shian Chiou , Shun-Hung Tsai, Ming-Tang Liu, A PSO-based adaptive fuzzy PID-controllers, Simulation Modelling Practice and Theory, Vol-26, 49-59, August 2012.
Hyoun-Surk Roh, Teramura Itaru, Observer-based wheelbase preview control of active vehicle suspensions, KSME International Journal, Volume 12( Issue 5): 782-79, September 1998.
Yoshimura Toshio, Teramura Itaru, Pneumatic active suspension control of a new one-wheel car model using single input rule modules fuzzy reasoning and a disturbance observer, Journal of Zhejiang University Science, Vol. 5(Issue 9): pp 1060-1068, September 2004.
Shao-BoLu, Yi-Nong-Li, Ling Zheng, Seung-Bok Choi, Study of Grey Predictive-Fuzzy control for MR Semi-Active Suspension system, International Conference on Mechanic Automation and Control, pp 26-28, June 2010.
Kun-Li Wen and Ting-Cheng Chang, The Research and Development of Completed GM(1,1) Model Toolbox Using Matlab , International Journal of Computational Cognition, Vol. 03 (Issue 03): 42-48,2005.
Niu Dong-xiao, Li Yan-chang, Zhang Qing, Research of Residual Error –Particle Swarm Optimization Gray Model Based on Markov in Load Forecasting, IEEE International Conference on Grey Systems and Intelligent Services, pp. 592-596, 2007.
Niu Dong-xiao, Li Yan-chang, Zhang Qing, Research of Residual Error –Ant Colony Optimization Gray Model Based on Markov in Load Forecasting, IEEE International Conference on Grey Systems and Intelligent Services, pp. 436-441,2008.
Cai-yun GAO, Ning GAO, A New Kind of Optimized Method of Grey Prediction Model and its Applications in Deformation, IEEE International Conference on Information Engineering and Computer Science, pp. 1-4, 2009.
Dan Ma, Qiang Zhang, Yan Peng, Shaojie Liu, A Particle Swarm Optimization Based Grey Forecast Model of Underground Pressure for Working Surface, Electronic Journal of Geotechnical Engineering, Vol. 16 (Bund. H): 811-830, 2011.
Ahmed, M and Svaricek, F., Adaptive robust gain scheduled control of vehicle semi-active suspension for improved ride comfort and road handling, IEEE International Conference on Mechatronics, pp.376-381, Feb. 27 2013-March 1 2013
Rajeswari.K and Lakshmi.P, Simulation of Suspension system with intelligent active force control, IEEE conference on ART Com, Kottayam, Kerala, pp.271-277, 15th- 16th October, 2010.
Deng.J.L, Introduction to Grey Theory, The Journal of Grey System, Vol-1, 1-24, 1989.
Zhou Shijian, Guan Yunlan, Lu Tieding, Weighted grey prediction model and its computational analysis, International Conference on Information Engineering and Computer Science, 2009. ICIECS 2009, pp.1,5, 19-20 Dec. 2009.
Kennedy, J.; Eberhart, R., Particle swarm optimization, Proceeding., IEEE International Conference on Neural Networks, Vol.4,pp.1942,1948 Nov/Dec 1995.
Gaing Z.L.,“A particle swarm optimization approach for optimum design of PID controller in AVR system”,IEEE Transactions on Energy conversion, Vol.19, No.2, pp. 384-391, 2004.
Wun Tong Sie, Ruey-Jing Lian, Bai-Fu Lin, Enhancing Grey Prediction Fuzzy Controller for Active Suspension System, Vehicle Sysem Dynamics: International Journal of Vehicle Mechanics and Mobility, Vol 44 (Issue 05), pp. 407-423, 19 August 2006.
Prabu, K., Jancirani, J., John, D., Dynamic characteristic analysis on half car electro pneumatic suspension system, (2013) International Review of Mechanical Engineering (IREME), 7 (3), pp. 436-441.
Aboud, W.S., Haris, S.M., A study on load dependent controller performance for mechatronic suspensions, (2012) International Review of Mechanical Engineering (IREME), 6 (3), pp. 611-616.
Rajinikanth, V., Latha, K., PID controller tuning for magnetic suspension system using evolutionary algorithm, (2012) International Review of Mechanical Engineering (IREME), 6 (5), pp. 988-995.
Refbacks
- There are currently no refbacks.
Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize
