PERMODELAN INVERSI PEREDAM MAGNET-REOLOGI BERBASIS JARINGAN SARAF TIRUAN UNTUK SISTEM KENDALI
Keywords:Magneto-Rheoloical Fluid, Magneto-Rheoloical Damper, Inverse Model, Artificial Neural Networks
The application of artificial neural network (ANN) models in magnet-rheological damper modeling is of great interest in recently challenges. Therefore, this study aims to propose a solution to overcome this problem by conducting inverse modeling using an artificial neural network. This inverse model is applied to a meandering magnet-rheological valve damper to predict the current to produce the appropriate damping force. The simulation scheme is selected with current as output and damping force, velocity, and displacement as input. The best model is formulated by varying the architecture of the artificial neural network. The best artificial neural network architecture is obtained after doing these variations. The data is divided into 80% training data, 10% validation data, and 10% test data. The activation function used is a logsig function using three hidden layers with the number of neurons in each layer [30-20-30]. The algorithm used in the chosen architecture is Levenberg-Marquardt. The regression value of 0.991 and the MSE value of 0.001 were obtained from the modeling results. The required damping force is ensured that it can be predicted well using the selected artificial neural network. The test proves that the results of the regression constant are 0.999 and the MSE value is 0.0005 when the current output value is inverted to the damper artificial neural network.
J. DE VICENTE, D.J. KLINGENBERG, R. HIDALGO-ALVAREZ, Magnetorheological fluids: a review, Soft Matter. 7 (2011) 3701. https://doi.org/10.1039/c0sm01221a.
B.F. SPENCER, S.J. DYKE, M.K. SAIN, J.D. CARLSON, Phenomenological Model for Magnetorheological Dampers, J. Eng. Mech. 123 (1997) 230–238. https://doi.org/10.1061/(ASCE)0733-9399(1997)123:3(230).
J.C. TUDON-MARTINEZ, R. MORALES-MENENDEZ, R. RAMIREZ-MENDOZA, L. GARZA-CASTANON, Experimental ANN-based modeling of an adjustable damper, in: 2014 Int. Jt. Conf. Neural Networks, IEEE, 2014: pp. 2512–2518. https://doi.org/10.1109/IJCNN.2014.6889391.
F. IMADUDDIN, S.A. MAZLAN, H. ZAMZURI, I.I.M. YAZID, Design and performance analysis of a compact magnetorheological valve with multiple annular and radial gaps, J. Intell. Mater. Syst. Struct. 26 (2015) 1038–1049. https://doi.org/10.1177/1045389X13508332.
M.R. JOLLY, J.W. BENDER, J.D. CARLSON, Properties and Applications of Commercial Magnetorheological Fluids, J. Intell. Mater. Syst. Struct. 10 (1999) 5–13. https://doi.org/10.1177/1045389X9901000102.
M.S. HOSSAIN, Z.C. ONG, Z. ISMAIL, S. NOROOZI, S.Y. KHOO, Artificial neural networks for vibration based inverse parametric identifications: A review, Appl. Soft Comput. J. 52 (2017) 203–219. https://doi.org/10.1016/j.asoc.2016.12.014.
N.R. FISCO, H. ADELI, Smart structures: Part I - Active and semi-active control, Sci. Iran. 18 (2011) 275–284. https://doi.org/10.1016/j.scient.2011.05.034.
M.M. NASERIMOJARAD, M. MOALLEM, S. ARZANPOUR, A comprehensive approach for optimal design of magnetorheological dampers, J. Intell. Mater. Syst. Struct. 29 (2018) 3648–3655. https://doi.org/10.1177/1045389X18798947.
K. TOH, M.G. ROMAY, F. SUN, K. TOH, M.G. ROMAY, K. MAO, Extreme Learning Machines 2013: Algorithms and Applications, Springer International Publishing, Cham, 2014. https://doi.org/10.1007/978-3-319-04741-6.
P.-S. KANG, J.-S. LIM, C. HUH, Artificial Neural Network Model to Estimate the Viscosity of Polymer Solutions for Enhanced Oil Recovery, Appl. Sci. 6 (2016) 188. https://doi.org/10.3390/app6070188.
L. MILAČIĆ, S. JOVIĆ, T. VUJOVIĆ, J. MILJKOVIĆ, Application of artificial neural network with extreme learning machine for economic growth estimation, Phys. A Stat. Mech. Its Appl. 465 (2017) 285–288. https://doi.org/10.1016/j.physa.2016.08.040.
G.G.S. DINATA, A.Z. MUTTAQIN, M. DARSIN, Rancang Bangun dan Uji Performa Sistem Kendali Pemberian Fluida Permesinan MQL Berbasis Arduino, J. Rekayasa Mesin. 11 (2020) 97–104. https://doi.org/10.21776/ub.jrm.2020.011.01.11.
I. BAHIUDDIN, F. IMADUDDIN, S.A. MAZLAN, M.H.M. ARIFF, K.B. MOHMAD, UBAIDILLAH, S. CHOI, Accurate and fast estimation for field-dependent nonlinear damping force of meandering valve-based magnetorheological damper using extreme learning machine method, Sensors Actuators A Phys. 318 (2021) 112479. https://doi.org/10.1016/j.sna.2020.112479.
F. IMADUDDIN, S.A. MAZLAN, UBAIDILLAH, M.H. IDRIS, I. BAHIUDDIN, Characterization and modeling of a new magnetorheological damper with meandering type valve using neuro-fuzzy, J. King Saud Univ. - Sci. 29 (2017) 468–477. https://doi.org/10.1016/j.jksus.2017.08.012.
S. NURUNNAHAR, D.B. TALUKDAR, R.I. RASEL, N. SULTANA, A short term wind speed forcasting using SVR and BP-ANN: A comparative analysis, in: 2017 20th Int. Conf. Comput. Inf. Technol., IEEE, 2017: pp. 1–6. https://doi.org/10.1109/ICCITECHN.2017.8281802.
I. BAHIUDDIN, S.B. WIBOWO, M. SYAIRAJI, J.T. PUTRA, C.A. PANDITO, A.F. MAULANA, R.M.S. PRASTICA, N. NAZMI, A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method, Fluids. 6 (2021) 76. https://doi.org/10.3390/fluids6020076.
N. NAZMI, M. AZIZI ABDUL RAHMAN, S. AMRI MAZLAN, D. ADIPUTRA, I. BAHIUDDIN, M. KASHFI SHABDIN, N. AFIFAH ABDUL RAZAK, M. HATTA MOHAMMED ARIFF, Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications, Open Eng. 11 (2020) 112–119. https://doi.org/10.1515/eng-2021-0009.
M.J.L. BOADA, J.A. CALVO, B.L. BOADA, V. DÍAZ, Modeling of a magnetorheological damper by recursive lazy learning, Int. J. Non. Linear. Mech. 46 (2011) 479–485. https://doi.org/10.1016/j.ijnonlinmec.2008.11.019.
Copyright (c) 2022 Rafly Asprilla Alwi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under aÂ Creative Commons Attribution-NonCommercial 4.0 International License.