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Decentralized neuro-fuzzy control for half car with semi-active suspension system

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Abstract

In this paper, a decentralized neuro-fuzzy controller has been created in order to improve the ride comfort and increase the stability for half car suspension system, which used the magneto-rheological damper as a semi-active device. Firstly, relative gain array and relative disturbance gain methods have been used for deriving a relation between inputs, disturbances and outputs to select pairing with minimum interaction to design a decentralize controller. Secondary, decentralized neuro-fuzzy controllers for front and rear chassis are designed to predict the required damping force taking the acceleration of the sprung mass and desired acceleration obtained by using pole-placement method as inputs. To predict the control voltage required for producing the force predicted by the controller, the inverse neuro-fuzzy model of MR damper has been designed. Simulation by using MATLAB programs has been created. The results show that the ride comforts and vehicle stability have been improved in comparison with the passive system.

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References

  • Ahmadian, M. T., Sharifi, S. R., Abdollahpour, R. (2005). Application of car active suspension in a vertical acceleration reduction of vehicle due to road excitation and its effect on human health. Int. J. Scientific Research(IJSR).

  • Ahmadian, M. and Simon, D. (1999). Application of magneto-rheological dampers for heavy truck suspensions. Proc. 32nd Int. Symp. Automotive Technology and Automation (ISATA), Vienna, Austria.

  • Bogdan, S. and Pawel, M. (2005). Vibration control in a pitch-plane suspension model with MR shock absorbers. J. Theoretical and Applied Mechanics 43,3, 655–674.

    Google Scholar 

  • Bristol, E. H. (1966). On a new measure of interactions for multivariable process control. J IEEE Trans. Autom. Control, AC-11, 133–134.

    Article  Google Scholar 

  • Cho, B.-K., Ryu, G. and Song, S. J. (2005). Control strategy of an active suspension for a half car model with preview information. Int. J. Automotive Technology 6,3, 243–249.

    Google Scholar 

  • Dhbaibi, S., Tlili, A. S. and Benhadj, N. (2008). Decentralized observer based H decentralized control for interconnected systems. ACSE J. 8,II, 43–53.

    Google Scholar 

  • Eltantawie, M. A. (2010). Forward and inverse fuzzy magnetorheological damper models for control purposes. ICGST-ACSE J. 10,1, 1–9.

    Google Scholar 

  • Errahmani, A., Benyakhlef, M. and Boumhidi, I. (2009). A robust decentralized control with unknown interconnections. ACSE J. 8,III, 9–14.

    Google Scholar 

  • Errahmani, A., Benyakhlef, M. and Boumhidi, I. (2009). Decentralized fuzzy control applied to a greenhouse. ACSE J. 9,II, 35–40.

    Google Scholar 

  • Fakharian, A., Mohammad, J. Y. P. and Alireza, A. (2007). Design of a nonlinear optimal controller for active suspension system in order to vertical motion control of automotive. J. Simulation Model, 25–37.

  • Feng, T. (2008). H-infinity PD controller for suspension systems with MR dampers. Proc. 47th IEEE Conf. Decision and Control, Cancun, Mexico, 4408–4413.

  • Ikhouane, F. and Rodellar, J. (2007). System with Hystresis: Analysis, Identification, and Control Using the Bouc-Wen Model. John Wiley&Sons Ltd. New York.

    Google Scholar 

  • International Organization for Standardization (1997). Mechanical Vibration and Shock Evaluation of Human Exposure to Whole-Body Vibration — Part 1: General Requirements. ISO.

  • Mailah, M. and Priyandoko, G. (2007). Simulation of a suspension system with adaptive fuzzy active force control. Int. J. Simulation Model, 25–36.

  • Mohammadi, A. K. (2005). Vehicle active suspension control, using a variable structure model reference adaptive controller. ACSE 05 Conf., CICC, Cairo, Egypt, 167–172.

  • Muthana, T. A. and Mohamed, Z. (2005). Decentralized controllers for power system load frequency control. ASCE J. 5,II, 7–22.

    Google Scholar 

  • Rajeswari, K. and Lakshmi, P. (2008). GA tuned distance based fuzzy sliding mode controller for vehicle suspension systems. Int. J. Engineering and Technology 5,1, 36–47.

    Google Scholar 

  • Rashid, M. M., Hussain, M. A. and Abd Rahim, N. (2006). Application of magneto-rheological damper for car suspension control. J. Applied Sciences 6,4, 933–938.

    Article  Google Scholar 

  • Renn, J. C. and Tsung-Han, W. (2007). Modeling and control of a new ¼ T servo-hydraulic vehicle active suspension system. J. Marine Science and Technology 15,3, 265–272.

    Google Scholar 

  • Seong, M. S., Choi, S. B. and Lee, H. G. (2009). Preview control of vehicle suspension system featuring MR shock absorber. 11th Conf. Electrorheolo-gical Fluids and Magnetorheological Suspensions, J. Physics: Conf. Series, 149, 012079.

    Google Scholar 

  • Shariati, A., Taghirad, H. D. and Fatehi, A. (2004). Decentralized Robust H Controller Design for a Half-Car Active Suspension System. University of Bath. ID-216. UK.

  • Shinq-Jen, W., Hsin-Han, C., Jiun-Hau, C. and Tsu-Tian, L. (2004). Optimal fuzzy control design for half-car active suspension systems. Proc. IEEE Int. Conf. Networking, Sensing& Control, Taipei, Taiwan, 583–588.

  • Smijith, G. B. and Jayaraj, K. (2009). MR damper and its application in vehicle suspension system. 10th National Conf. Technological Trends (NCTT09), 69–74.

  • Song, X. and Ahmadian, M. (2004). Study of semi-active adaptive control algorithms with magneto-rheological seat suspension. SAE Technical Paper No. 01-1648.

  • Spencer, B. F., Dyke, S. J., Sain, M. K. and Carlson, J. D. (1997). Phenomenological model for magnetorheological dampers. J. Engineering American Society of Civil Eng. 123,3, 230–352.

    Google Scholar 

  • Stanley, G., Marino-Galarraga, M. and McAvoy, T. J. (1985). Short-cut operability analysis: The relative disturbance gain. Ind. Eng. Chem. Process Des., 24, 1181–1188.

    Article  Google Scholar 

  • Støíbrský, A., Hyniová, K., Honcù J. and Aleš, K. (2003). Using fuzzy logic to control active suspension system of one-half-car model. ACTA Montanistica Slovaca Roèník, 223–227.

  • Suaib, N. M. and Sam, Y. M. (2008). Modeling and control of active suspension using PISMC and SMC. J. Mechanical, 26, 119–128.

    Google Scholar 

  • Sun, P. Y. and Chen, H. (2003). Multi-objective out-putfeedback suspension control on a half-car model. Proc. IEEE Conf. Control Applications, 1, 290–295.

    Google Scholar 

  • Tomoaki, M., Itthisek, N. and Akira, S. (2007). Adaptive semi-active control of suspension system with MR damper. Proc. IFAC Workshop on Adaptive Learning Control and Signal Processing, Russia.

  • Vipul, S. and Roschke, P. N. (2004). Neuro-fuzzy control of railcar vibrations using semi-active damper. Computer Aided Civil and Infrastructure Engineering, 19, 81–92.

    Article  Google Scholar 

  • Wang, E., Xiao, M., Rakheja, S. and Su, C. Y. (2003). Semi-active control of vehicle vibration with MR dampers. Proc. 42nd IEEE Conf. Decision and Control, Maui, Hawaii, USA, 2270–2275.

  • Yahaya, M. S. and Osman, J. H. (2004). Robust controller for active suspension with hydraulic dynamics. 5th Asian Control Conf., 1, 598–60

    Google Scholar 

  • Yahaya, M. S. and Osman, J. H. (2006). Sliding mode control of a hydraulically actuated active suspension. J. Technology, 37–48.

  • Yu, M., Dong, X.-M., Liao, C.-R and Chen, W.-M. (2006). Adaptive fuzzy-neural network control for magnetorheological suspension. Int. J. Computer Science and Network Security 6,10, 66–71.

    Google Scholar 

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Authors and Affiliations

  1. Mechanical Engineering Department, Higher Technological Institute, 6th October Branch, Giza, Egypt

    M. A. Eltantawie

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  1. M. A. Eltantawie
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Correspondence to M. A. Eltantawie.

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Eltantawie, M.A. Decentralized neuro-fuzzy control for half car with semi-active suspension system. Int.J Automot. Technol. 13, 423–431 (2012). https://doi.org/10.1007/s12239-012-0039-y

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  • DOI: https://doi.org/10.1007/s12239-012-0039-y

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