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Comparing the performance of using a smart damper in a semi-active ‎‎suspension instead of a traditional damper using MATLAB/Simulink

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EN
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EN
Given the importance of comfort and safety in various driving circumstances, the suspension system emerges as the most ‎crucial component. Two different suspension systems, passive (PSS) and semi-active (SASS), are compared for effectiveness in ‎this research. MATLAB/Simulink is used for simulation, employing a representative two-degree-of-freedom car model to ‎evaluate and compare the performance results of these systems. The differential equations of motion for the two systems are ‎modeled and simulated using software, which illuminates how they would behave under the same parameters and ‎circumstances. Additionally, a Magnetorheological damper (MR) model with a ¼ vehicle system is used to evaluate its behavior ‎on various types of roads, including those with steps, bumps, and random inputs. This study utilizes the Bingham plastic model ‎to compare the simulation results of SASS and PSS systems. After comparing the numerical and graphical results from the two ‎systems, it is observed that SASSs with controllers perform better than PSSs in terms of suspension adjustment and response ‎time. The SASS is superior to the PSS in suppressing oscillations by 55.12%, 77.47%, and 86.78% for step input, bump, and ‎random inputs, respectively. Additionally, the SASS is faster in eliminating oscillations compared to the PSS by 54% and 51.7% ‎for step input and bump inputs, respectively. ‎ ‎
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1--16
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
  • Department of Mechanical Engineering, College of Engineering, University of Baghdad, IRAQ
  • Department of Mechanical Engineering, College of Engineering, University of Baghdad, IRAQ,
Bibliografia
  • [1] Al-Ashtari W. (2023): Fuzzy logic control of active suspension system equipped with a hydraulic actuator.– Int. J.Appl. Mech. Eng., vol.28, No.3, pp.13-27, doi: 10.59441/ijame/172895.
  • [2] Al-Araji H.M.H., Al-Zughaibi A.I.H. and Hussein E.Q. (2023): Testing two types of magneto-rheological (MR) dampermodels with quarter car suspension system response.– Int. J. Tech. Phys. Probl. Eng., vol.15, No.1, pp.52-61.
  • [3] Wu J., Yang D., Cao W., Sun J., Wang Y. and Cao W.Z. (2023): Simulation study of semi-active suspension fuzzyadaptive PID control system.– J. Phys. Conf. Ser., vol.2501, No.1, doi: 10.1088/1742-6596/2501/1/012040.
  • [4] Bhise A.R., Desai R.G., Yerrawar M.R.N., Mitra A.C. and Arakerimath D.R.R. (2016): Comparison between passiveand semi-active suspension system using Matlab/Simulink.– IOSR J. Mech. Civ. Eng., vol.13, No.04, pp.01-06, doi:10.9790/1684-1304010106.
  • [5] Kumar S., Medhavi A., Kumar R., and Mall P.K. (2022): Modeling, analysis and PID controller implementation onsuspension system for quarter vehicle model.– Journal of Mechanical Engineering and Sciences, vol.16, No.2.,pp.8905-8916, doi: 10.15282/jmes.16.2.2022.08.0704.
  • [6] Sinjari S. (2023): Comparing Optimization Algorithms for Parameter Identification of Sigmoid Experimental Studyon The Behaviour of a Magnetorheological (MR) Damper and Evaluation of Numerical Models.– Electronic Thesesand Dissertations, University of Windsor, https://scholar.uwindsor.ca/etd/9309.
  • [7] Silva D.M.D., Avila S., Morais M.V.G. and Cavallini A.A. Jr (2023): Comparing optimization algorithms forparameter identiication of sigmoid model for MR damper comparing optimization algorithms for parameteridentification of sigmoid model for MR damper.– No.6, doi: 10.21203/rs.3.rs-2898815/v1.
  • [8] Sassi S., Sassi A., Cherif K. and Tarlochan F. (2018): Magnetorheological damper with external excitation for moreefficient control of vehicles dynamics.– J. Intell. Mater. Syst. Struct., vol.29, No.14, pp.2919-2932, doi:10.1177/1045389X18781038.
  • [9] Braz-Cesar M.T. and Barros R.C. (2010): Semi-active vibration control of buildings using MR dampers : numericaland experimental verification.– 14th Eur. Conf. Earthq. Eng., p.829.
  • [10] Pepe G., Roveri N. and Carcaterra A. (2019): Experimenting sensors network for innovative optimal control of carsuspensions.– Sensors (Switzerland), vol.19, No.14, pp.14-17, doi: 10.3390/s19143062.
  • [11] Kang B.H., Jo B.H., Kim B.G., Hwang J.H. and Choi S.B. (2023): Linear and nonlinear models for drop simulationof an aircraft landing gear system with MR dampers.– Actuators, vol.12, No.7, doi: 10.3390/act12070287.
  • [12] Ochoa-Diaz C., Rocha T.S., Oliveria L.L. and Paredes M.E. (2014): An above-knee prosthesis withmagnetorheological variable-damping.– Proc. IEEE RAS EMBS Int. Conf. Biomed. Robot. Biomechatronics, No.8,pp.108-113, doi: 10.1109/biorob.2014.6913761.
  • [13] Abdul Aziz M., Muhtasim S. and Ahammed R. (2022): State-of-the-art recent developments of largemagnetorheological (MR) dampers.– Korean Society of Rheology, Australian Society of Rheology, vol.34, No.2,doi: 10.1007/s13367-022-00021-2.
  • [14] Zhang Y., Guo J., Yang J. and Li X. (2023): Recent structural developments and applications of magnetorheologicaldampers (MRD): a review.– Magnetochemistry, vol.9, No.4, doi: 10.3390/magnetochemistry9040090.
  • [15] Elderrat H.I. (2013): Research Towards the Design of a Novel Smart Fluid Damper Using a McKibben Actuator.–Submitted for the degree of Master of Philosophy, University of Sheffield, p.65.
  • [16] Kim B.G., Yoon D.S., Kim G.W., Choi S.B., Tan A.S., and Sattel T. (2020): Design of a novel magnetorheologicaldamper adaptable to low and high stroke velocity of vehicle suspension system.– Appl. Sci., vol.10, No.16, doi:10.3390/app10165586.
  • [17] Zhang S., Shi W., and Chen Z. (2021): Modeling and parameter identification of MR damper considering excitationcharacteristics and current.– Shock Vib., vol.2021, No.3, doi: 10.1155/2021/6691650.
  • [18] Yang G., Spencer B.F., Carlson J.D. and Sain M.K. (2002): Large-scale MR fluid dampers: modeling and dynamicperformance considerations.– Eng. Struct., vol.24, No.3, pp.309-323, doi: 10.1016/S0141-0296(01)00097-9.
  • [19] Wang D.H. and Liao W.H. (2011): Magnetorheological fluid dampers: a review of parametric modelling.– SmartMater. Struct., vol.20, No.2, doi: 10.1088/0964-1726/20/2/023001.
  • [20] Spencer Jr B.F., Dyke S.J., Sain M.K. and Carlson J.D. (1996): Phenomenological model of a magnetorheologicaldamper.– J. Eng. Mech., vol.230-238, No.123, pp.1-23, doi: 10.1061/(ASCE)0733-9399(1997)123.
  • [21] Jamil M., Zafar S., and Gilani S.O. (2018): Designing PID controller based semi-active suspension system usingMATLAB simulink.– Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST, vol.224, No.7, pp.282-295,doi: 10.1007/978-3-319-94180-6_27.
  • [22] Riazi B. (2021): Design and Investigation of a Semi-Active Suspension System in Automotive Applications. –Electronic Theses and Dissertations, University of Windsor, https://scholar.uwindsor.ca/etd/8683.
  • [23] Eshkabilov S. (2016): Modeling and simulation of non-linear and hysteresis behavior of magneto-rheologicaldampers in the example of quarter-car model.– Engineering Mathematics, vol.1, No.1, pp.19-38, doi:10.11648/j.engmath.20160101.12.
  • [24] Ismaili N. (2019): Performance analysis of passive, semi-active and active-controlled suspension systems usingMATLAB / SIMULINK.– Journal of Applied Sciences-Sut, vol.5, No.9, pp.94-105.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6c8f0c5e-07dc-4290-b2df-6ce0fff1cc8b
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