Modeling of Magnetorheological Damper Using Back Propagation Neural Network

Ubaidillah Ubaidillah; Gigih Priyandoko; Muhammad Nizam; Iwan Yahya

doi:10.4028/www.scientific.net/AMR.896.396

Paper Titles

Determination of Thin Film Ba_0.5Sr_0.5TiO₃ Ferroelectric Effective Mass from I-V Characteristics Calculation Using Transfer Matrix Method
p.375

Thermal Analysis and Magnetic Properties of Lanthanum Barium Manganite Perovskite
p.381

Magnetoelectric Coupling Phenomena Based on the Changes of Magnetic Properties in Multiferroic Nanocomposite BaTiO₃-BaFe₁₂O₁₉
p.385

Microstructural Studies of (Ba_0.7Sr_0.3Fe₁₂O₁₉)_1-x - (Ba_0.7Sr_0.3TiO₃)_xwith x = 0.2, x = 0.5 and x = 0.8 Composite System by Mechanical Alloying Process
p.391

Modeling of Magnetorheological Damper Using Back Propagation Neural Network
p.396

Microstructural and Magnetic Properties of Ti²⁺-Mn⁴⁺ Substituted Barium Hexaferrite
p.401

The Influence of Ni-Doping on Structure and Magnetic Properties of La_0.67Ba_0.33MnO₃
p.406

Micromagnetic Study on the Dynamic Susceptibility Spectra of Square-Patterned Ferromagnets
p.410

Micromagnetic Simulation on Ground State Domain Structures of Barium Hexaferrite (BaFe₁₂O₁₉)
p.414

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 896Modeling of Magnetorheological Damper Using Back...

Modeling of Magnetorheological Damper Using Back Propagation Neural Network

Abstract:

This paper presents a new approach to model magneto-rheological (MR) dampers for semi-active suspension systems. The neural network method using adaptive back-propagation learning algorithm real is proposed. The experimental data collected from suspension test machine consist in time histories of current, displacement, velocity and force measured both for constant and variable current. The model parameters are determined using a set of experimental measurements corresponding to different current constant values. It has been shown that the damper response can be satisfactorily predicted with this model.

You might also be interested in these eBooks

Advanced Materials Science and Technology

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 896)

Pages:

396-400

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.896.396

Citation:

Cite this paper

Online since:

February 2014

Authors:

Ubaidillah Ubaidillah*, Gigih Priyandoko, Muhammad Nizam, Iwan Yahya

Keywords:

Magneto-Rheological Damper, Neural Network (NN), Semi-Active Suspension

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Ubaidillah, K. Hudha, F. A. A. Kadir, Modeling, Characterization and Force Tracking Control of a Magnetorheological Damper under Harmonic Excitation, Int. J. of Modeling Identification and Control 13 (2011) 9021.

DOI: 10.1504/ijmic.2011.040485

Google Scholar

[2] A. Dominguez, R. Sedaghati, I. Stiharu, Modeling and application of MR dampers in semi-adaptive structures, Computers and Structures 86 (2008) 407–415.

DOI: 10.1016/j.compstruc.2007.02.010

Google Scholar

[3] H. Dua, J. Lamb, N. Zhang, Modelling of a magneto-rheological damper by evolving radial basis function networks, Engineering Applications of Artiﬁcial Intelligence 19 (2006) 869–881.

DOI: 10.1016/j.engappai.2006.02.005

Google Scholar

[4] K. C. Schurter, P. N. Roschke, Fuzzy modeling of a magnetorheological damper using ANFIS, Proceedings of IEEE International Conference on Fuzzy Systems (2000) 122–127.

DOI: 10.1109/fuzzy.2000.838645

Google Scholar

[5] B. M. Wilamowski, Neural network architectures and learning, IEEE International Conference on Industrial Technology 1 (December 2003) 10-12.

Google Scholar

[6] B. M. Wilamowski, Y. Chen, A. Malinowski, Efficient algorithm for training neural networks with one hidden layer, Proc. IJCNN, vol. 3, 1999, pp: 1725-1728.

DOI: 10.1109/ijcnn.1999.832636

Google Scholar