This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Application of a Preview Control with an MR Damper Model Using Genetic Algorithm in Semi-Active Automobile Suspension
ISSN: 0148-7191, e-ISSN: 2688-3627
Published February 5, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: Automotive Technical Papers
A non-linear mathematical model of a semi-active (2DOF) vehicle suspension using a magnetorheological (MR) damper with information concerning the road profile ahead of the vehicle is proposed in this paper. The semi-active vibration control system using an MR damper consists of two nested controllers: a system controller and a damper controller. The fuzzy logic technique is used to design the system controller based on both the dynamic responses of the suspension and the Padé approximation algorithm method of a preview control to evaluate the desired damping force. In addition, look-ahead preview of the excitations resulting from road irregularities is used to quickly mitigate the effect of the control system time delay on the damper response. Adaptive neuro-fuzzy inference system (ANFIS) inverse model without preview, ANFIS inverse model with preview, and ANFIS inverse model with preview and optimization strategies are used to design the damper controller to evaluate different values of the command voltage based on the tracking of a desired damping force to compare which of them gave the best behavior of the MR damper. Each one of these strategies is used in conjunction with the system controller to evaluate the effectiveness of a damper controller design on semi-active control. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road disturbance. The simulation results prove that the proposed strategy of the ANFIS inverse model with preview and optimization on MR damper produces a smoother and lower input voltage to the MR damper coil, ensuring extended damper life and lower power requirement, respectively. The compared results reveal that although the ANFIS inverse model with preview and optimization is able to improve ride comfort and vehicle stability over other mentioned strategies for semi-active suspension system or even passive suspension system.
CitationShehata Gad, A., El-Zoghby, H., Oraby, W., and Mohamed El-Demerdash, S., "Application of a Preview Control with an MR Damper Model Using Genetic Algorithm in Semi-Active Automobile Suspension," SAE Technical Paper 2019-01-5006, 2019, https://doi.org/10.4271/2019-01-5006.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
|[Unnamed Dataset 4]|
- Marzbanrad, J., Ahmadi, G., Zohoor, H., and Hojjat, Y. , “Stochastic Optimal Preview Control of A Vehicle Suspension,” Journal of Sound and Vibration, 973-990, 2004.
- Surk-Roh, H. and Park, Y. , “Observer-Based Wheelbase Preview Control of Active Vehicle Suspensions,” Ksme International Journal, 782-791, 1998.
- Youn, I., Khan, M.A., Uddin, N., Youn, E. et al. , “Road Disturbance Estimation for the Optimal Preview Control of an Active Suspension Systems Based on Tracked Vehicle Model,” International Journal of Automotive Technology, 18:307-316, 2017.
- Askari, M. and Davaie-Markazi, H A. , “Multi-Objective Preview Control of Active Vehicle Suspensions,” in The International Federation of Automatic Control, Seoul, Korea, 2008.
- Ryu, S., Kim, Y., and Park, Y. , “Robust H∞ Preview Control of an Active Suspension System with Norm-Bounded Uncertainties,” International Journal of Automotive Technology, 585-592, 2008.
- Wang, D.H. and Liao, W.H. , “Semi-active Controllers for Magneto-Rheological Fluid Dampers,” Journal of Intelligent Material Systems and Structures, 983-993, 2005.
- Metered, H., Bonello, P., and Oyadiji, S.O. , “An Investigation Into the Use of Neural Networks for the Semi-Active Control of a Magnetorheologically Damped Vehicle Suspension,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 224:829-848, 2010.
- Wang, H. , “Modeling of Magneto-Rheological Damper Using Neuro-Fuzzy System,” Springer-Verlag Berlin Heidelberg, in AISC 62 2 (2009): 1157-1164.
- Ahn, K.K., Truong, D.Q., and Islam, M.A. , “Modeling of A Magneto-rheological (MR) Fluid Damper using A Self-Tuning Fuzzy Mechanism,” Journal of Mechanical Science and Technology, 1485-1499, 2009.
- Metered, H., Bonello, P., and Oyadiji, S.O. , “The Experimental Identification of Magnetorheological Dampers and Evaluation of Their Controllers,” Mechanical Systems and Signal Processing, 24:976-994, 2010.
- Askari, M. and Davaie-Markazi H.A. , “Multi-Objective Optimal Fuzzy Logic Controller for NON-Linear Building-MR Damper System,” in 5th International Multi-Conference on Systems ,Signals and Devices, 2008.
- Zong, L.H., Gong, X.L., Guo, C.Y., and Xuan, S.H. , “Inverse Neuro-Fuzzy MR Damper Model and Its Application in Vibration Control of Vehicle Suspension System, Vehicle System Dynamics,” International Journal of Vehicle Mechanics and Mobility, 1025-1041, 2012.
- Nugroho, P.W., Li, W., Du, H., Alici, G. et al. , “An Adaptive Neuro Fuzzy Hybrid Control Strategy for a Semi-Active Suspension with Magneto-Rheological Damper,” Advances in Mechanical Engineering, 2014:1-11, 2014.
- Atray, V.S. and Roschke, P.N. , “Neuro-Fuzzy Control of Railcar Vibrations Using Semi-Active Damper,” Computer-Aided Civil and Infrastructure Engineering, 19:81-92, 2004.
- Tao, S., Zhen-y, H., Da-yue, C., and Lei, T. , “Signal Frequency Based Self-Tuning Fuzzy Controller for Semi-Active Suspension System,” Journal of Zhejiang University Science, 426-432, 2003.
- Du, H., Tang, X., Du, H., Sun, S. et al. , “Takagi-Sugeno Fuzzy Control for Semi-Active Vehicle Suspension with a Magneto-Rheological Damper and Experimental Validation,” ASME Transactions on Mechatronics, 1083-4435, 2016.
- Zareh, S.H., Abbasi, M., Mahdavi, H., and Osgouie, K.G. , “Semi-Active Vibration Control of An Eleven Degrees of Freedom Suspension System Using Neuro Inverse model of Magneto-Rheological Dampers,” Journal of Mechanical Science and Technology, 2459-2467, 2012.
- Aggarwal, D.M. , “Fuzzy Control of Passenger Ride Performance Using MR Shock Absorber Suspension in Quarter Car Model,” International Journal of Dynamics and Control, 3:463-469, 2015.
- Devdutt and Aggarwal, D.M. , “Fuzzy Control of Semi-Active Quarter Car Suspension System with MR Damper,” in Proceedings of The National Conference on Trends and Advances In Mechanical Engineering, Faridabad, Haryana, 2012.
- Dong, X.Z. and Qing, G.Y. , “Integrated Intelligent Control Analysis on Semi-Active Structures By Using Magneto-Rheological Dampers,” Science in China Series E: Technological Sciences, 2280-2294, 2008.
- Dong, X.M., Miao, Y., Liao, C.R., and Chen, W.M. , “Comparative Research on Semi-Active Control Strategies for Magneto-Rheological Suspension,” Nonlinear Dynamics, 433-453, 2010.
- Wilson, C.M. and Abdullah, M.M. , “Structural Vibration Reduction Using Self-Tuning Fuzzy Control of Magnetorheological Dampers,” Bull Earthquake Engineering, 1037-1054, 2010.
- Li, C. and Zhao, Q. , “Simulation of Fuzzy Control on Automobile Semi-active Suspension with MR Damper,” in International Conference on ICCE2011, AISC 111, Harbin, China, 2011.
- Gupta, S., K, R., Sonawane, V., and Sudhakar, D.D. , “Optimization of Vehicle Suspension System Using Genetic Algorithm,” International Journal of Mechanical Engineering and Technology, 6:47-55, 2015.
- Jabeen, S.D. , “Vibration Optimization of A Passive Suspension System via Genetic Algorithm,” International Journal of Modeling, Simulation, and Scientific Computing, 1-21, 2013.
- Tang, C.Y., Zhao, G.Y., Li, H., and Zhou, S.W. , “Research on Suspension System Based on Genetic Algorithm and Neural Network Control,” in 2009 Second International Conference on Intelligent Computation Technology and Automation, Shenyang, China, 2009.
- Nagarkar, M.P. and Vikhe Patil, G.J. , “Multi-Objective Optimization of LQR Control Quarter Car Suspension System using Genetic Algorithm,” FME Transactions, 44:187-196, 2016.
- You, I., Tchamn, R., Lee, S.H., Uddin, N. et al. , “Preview Suspension Control for a Full Tracked Vehicle,” International Journal of Automotive Technology, 15:399-410, 2014.
- Abd-Ei-Tawwab, A.M. , “Twin Accumulator Semi Active Suspension System with Preview Control,” Journal of Low Frequency Noise, Vibration and Active Control, 26:283-293, 2007.
- Burns, R.S. , Advanced Control Engineering (Oxford: Elsevier, 2001).
- Fischer, D. and Isermann, R. , “Mechatronic Semi-Active and Active Vehicle Suspensions,” Control Engineering Practice 12:1353-1367, 2004.
- Metered, H., Kozek, M., and Šika, Z. , “Vibration Control of Active Vehicle Suspension Using Fuzzy Based Sliding Surface,” International Journal of Fuzzy Systems and Advanced Applications, 41-48, 2015.
- Choi, S.B. and Sung, K.G. , “Vibration Control of Magneto-rheological Damper System Subjected to Parameter Variations,” International Journal of Vehicle Design, 46, 2008.
- Sammier, D., Sename, O., and Dugard, L. , “Skyhook and H∞ Control of Semi-active Suspensions,” Some Practical Aspects, Vehicle System Dynamics, 39:279-308, 2003.
- Metered, H. , “Application of Nonparametric Magnetorheological Damper Model in Vehicle Semi-Active Suspension System,” SAE Int. J. Passeng. Cars - Mech. Syst. 5(1):715-726, 2012, doi:10.4271/2012-01-0977.
- Konieczny, L. , “Analysis of Simplifications Applied in Vibration Damping Modelling for a Passive Car Shock Absorber,” 2016:1-9, 2016, doi:10.1155/2016/6182847.
- Shehata, A., Metered, H., and Oraby, W.A.H. , “Vibration Control of Active Vehicle Suspension System using Fuzzy Logic Controller,” Vibration Engineering and Technology of Machinery, Mechanisms and Machine Science, 23:389-399, 2015.
- Shehata, A., El-Zoghby, H.M., Oraby, W.A.H., and El-Demerdash, S.M. , “Performance and Behaviour of a Magneto-Rheological Damper in a Semi-Active Vehicle Suspension and Power Evaluation,” American Journal of Mechanical Engineering and Automation, 5(2381-6198 (Print)):72-89, 2018.
- Pacejka, H.B. and Besselink , “Magic Formula Tyre Model With Transient Properties,” in Proceeding of 2nd Colloquium on Tire Models For Vehicle Analysis, 1996, Vol. 27.