Abstract
Research and development process for automobile industry is costly and time consuming. Hence, simulation is a common approach for solving real-world problems, cheaper, faster, and easier at a risk free environment. Simulations help to reduce costs and time, because of its fast and easy design and run capability, especially for automotive applications. However, simulation models’ accuracy depends on the mathematical models used and assumptions made. The accuracy of the simulation model for any test bench is crucial since the results are going to shape the critical design of the actual systems. In this study, fuzzy logic approach is used to improve the simulation model of the 5 degree of freedom (DOF) servo hydraulic leaf spring test bench in Mercedes Benz Türk Aksaray Truck Plant. A multi-body simulation (MBS) software named Simpack is already being used by the company for analyses. However, the results of the simulations do not fully correlate with the results of experiments for identical scenarios. Since Simpack control toolbox is inadequate for a controller design for this complicated test bench, Fuzzy Logic (FL) toolbox in Simulink has been used to improve the results of the MBS model. First, the test bench has been calibrated and several experiments are conducted in which servo hydraulic pistons apply force on spring leafs and the displacement data are acquired. Second, a Fuzzy Logic Controller (FLC) is designed to evaluate the piston forces that correspond to the actual displacement data. Lastly, the FLC is implemented to the closed plant model of the test bench in Matlab/Simulink and simulations are performed. The results revealed that the simulation results are a better match to experimental data when FLC is employed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Giannakis, E., Malikoutsakis, M., Savaidis, G.: Fatigue design of leaf springs for new generation trucks. In: 20th Innovative Manufacturing Engineering and Energy Conference 2016. IOP Publishing, Greece (2016)
Dhumal, P.D., Jadhav, M.S., Tawar, R.S., Agrawal, S.A.: Review on leaf spring type suspension system. Int. J. Innov. Res. Sci. Eng. Technol. (2017)
Omar, M.A., Shabana, A.A., Mikkola, A., Loh, W.Y., Basch, R.: Multibody system modeling of leaf springs. J. Vib. Control 10(11), 1601–1638 (2004)
Simpack Homepage. http://www.simpack.com. Last accessed 20 Feb 2019
Ghuku, S., Kashinath, S.: Design development and performance analysis of leaf spring testing set up in elastic domain. J. Assoc. Eng. 86(1–2), 23–41 (2016)
Hao, K.M., Xu, W.B., Li, B., Feng, X.L.: Design of test bench for automotive leaf spring. Advanced Materials Research 834–836, 1718–1722 (2013)
Kanbolat, A., Soner, M., Erdogus, T., Karaagac, M.: Endurance rig tests and non linear finite element analysis. In: SAE Technical Paper Series, pp. 1–8 (2011)
Kırlı, A., Ömürlü, V.E., Büyüksahin, U., Artar, R., Ortak, E.: Self tuning fuzzy PD application on TI TMS320F28335 for an experimental stationary quadrotor. In: 4th European Education and Research Conference, pp. 42–46 (2010)
Kırlı, A., Büyükşahin, U.: Fuzzy logic modelling of a transparent and elastic silicone pad for a new and novel optic based tactile sensor. Adv. Mater. Process. Technol. 1, 306–315 (2016)
Zhu, X., Liu, Z., Yang, J.: Research on co-simulation method in ADAMS and MATLAB for missile seeker’s stabilization platform design. In: Tan, G., Yeo, G.K., Turner, S.J., Teo, Y.M. (eds.) Communications in Computer and Information Science AsiaSim 2013, vol. 402, pp. 105–113. Springer, Berlin, Heidelberg (2013)
Verbruggen, H.B., Bruijn, P.M.: Fuzzy control and conventional control: what is (and can be) the real contribution of fuzzy systems. Fuzzy Sets Syst. 90(2), 151–160 (1997)
Liu, B.D., Huang, C.Y.: Design and implementation of the tree-based fuzzy logic controller. IEEE Trans. Syst. Man, Cybern. 27, 475–487 (1997)
Bei, S.Y., Zhao, J.B., Zhang, L.C., Liu, S.H.: Fuzzy control and co-simulation of automobile semi-active suspension system based on SIMPACK and MATLAB. Appl. Mech. Mater. 39, 50–54 (2010)
Oba, S., Sato, M.A., Takemasa, I., Monden, M., Matsubara, K.I., Ishii, S.: A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19, 2088–2096 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Üstünışık, E., Kırlı, A., Er, İ.O., Erginsoy, Y.E. (2020). Improvement of the Simulation Model for High Accuracy Leaf Spring Test Bench by Implementing Fuzzy Logic. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_156
Download citation
DOI: https://doi.org/10.1007/978-3-030-23756-1_156
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23755-4
Online ISBN: 978-3-030-23756-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)