Abstract
Maglev is a system in which the train runs levitated from the guideway by using electromagnetic forces between superconducting magnets (ferromagnetic materials) on board of the train and coils on the ground. The magnetic levitation train system are based on two types, electrodynamics suspension (EDS) and electromagnetic suspension (EMS). EDS is based on repulsive forces acting on a magnet and is inherently stable system and even has well robustness in many cases with open loop control. In this paper, we have assumed the EMS based train system. The electromagnetic suspension system is based on attractive forces acting on a magnet and is complex, unstable and the model is strongly nonlinear. In addition, due to the external disturbances like wind, the unbalanced magnetic forces between the guideway and the train, and parameter perturbation, the system model has greater uncertainty. This paper presents a hybrid neuro-fuzzy controllers for the magnetic levitation train system. The controllers are designed to bring the magnetic levitation system in a stable region by keeping the train suspended in the air in the required position in the presence of uncertainties. PID controller is used to generate the data which requires to train the hybrid controllers. The performance and robustness of the controllers have been compared by simulating the system with disturbances. After implementing and validating, the Matlab simulation results show that the performance of the system (overshoot, settling time, rise time and peak response) have improved and the controller have good robustness and adaptability.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sharkawy, A.B., Abo-Ismail, A.A.: Intelligent control of magnetic levitation system. J. Eng. Sci. Assiut Univ. 37(4), 909–924 (2009)
Liu, Z., Long, Z., Li, X.: Maglev train overview. In: Liu, Z., Long, Z., Li, X. (eds.) Maglev Trains, pp. 1–28. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-45673-6_1
Tandan, G.K., Sen, P.K., Sahu, G., Sharma, R., Bohidar, S.: A review on development and analysis of maglev train. Int. J. Res. Advent Technol. 3(12), 14–17 (2015)
Bajuri, M.F.: Modelling magnetic levitation (maglev) train. Ph.D. thesis, UMP (2012)
Magnetic levitation train system. http://techdatacare.blogspot.com/2011/12/magnetic-levitation.html. Accessed 1 Sept 2017
Cabral, T., Chavarette, F.: Dynamics and control design via LQR and SDRE methods for a maglev system. Int. J. Pure Appl. Math. 101(2), 289–300 (2015)
Choudhary, S.K.: Robust feedback control analysis of magnetic levitation system. WSEAS Trans. Syst. 13(27), 285–291 (2014)
Pati, A., Pal, V.C., Negi, R.: Design of a 2-DoF control and disturbance estimator for a magnetic levitation system. Eng. Technol. Appl. Sci. Res. 7(1), 1369 (2016)
Ahmad, I., Javaid, M.A.: Nonlinear model & controller design for magnetic levitation system. In: Recent Advances in Signal Processing, Robotics and Automation, pp. 324–328 (2010)
Sun, Y., Qiang, H., Lin, G., Ren, J., Li, W.: Dynamic modeling and control of nonlinear electromagnetic suspension systems. Chem. Eng. Trans. 46, 1039–1044 (2015)
Al-Hmouz, A., Shen, J., Al-Hmouz, R., Yan, J.: Modeling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Trans. Learn. Technol. 5(3), 226–237 (2012)
Panda, G., Panda, S., Ardil, C.: Hybrid neuro fuzzy approach for automatic generation control of two-area interconnected power system. Int. J. Comput. Intell. 5(1), 80–84 (2009)
Kaur, A., Kaur, A.: Comparison of fuzzy logic and neuro-fuzzy algorithms for air conditioning system. Int. J. Soft Comput. Eng. 2(1), 417–420 (2012)
Walia, N., Singh, H., Sharma, A.: ANFIS: adaptive neuro-fuzzy inference system - a survey. Int. J. Comput. Appl. 123(13), 32–38 (2015)
Vieira, J., Dias, F.M., Mota, A.: Neuro-fuzzy systems: a survey. In: 5th WSEAS NNA International Conference on Neural Networks and Applications, Udine, Italia (2004)
Allaoua, B., Laoufi, A., Gasbaoui, B., Abderrahmani, A.: Neuro-fuzzy DC motor speed control using particle swarm optimization. Leonardo Electron. J. Pract. Technol. 15, 1–18 (2009)
Kusagur, A., Kodad, S., Ram, B.V.S.: Modeling, design & simulation of an adaptive neuro-fuzzy inference system (ANFIS) for speed control of induction motor. Int. J. Comput. Appl. 6(12), 29–44 (2010)
Sivakumar, R., Sahana, C., Savitha, P.: Design of ANFIS based estimation and control for mimo systems. Int. J. Eng. Res. Appl. 2(3), 2803–2809 (2012)
Yousef, H.A., Khalfan, A.K., Albadi, M.H., Hosseinzadeh, N.: Load frequency control of a multi-area power system: an adaptive fuzzy logic approach. IEEE Trans. Power Syst. 29(4), 1822–1830 (2014)
Rashid, U., Jamil, M., Gilani, S.O., Niazi, I.K.: LQR based training of adaptive neuro-fuzzy controller. In: Bassis, S., Esposito, A., Morabito, F.C., Pasero, E. (eds.) Advances in Neural Networks. SIST, vol. 54, pp. 311–322. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33747-0_31
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Teklehaimanot, Y.K., Negash, D.S., Workiye, E.A. (2019). Design of Hybrid Neuro-Fuzzy Controller for Magnetic Levitation Train Systems. In: Mekuria, F., Nigussie, E., Tegegne, T. (eds) Information and Communication Technology for Development for Africa. ICT4DA 2019. Communications in Computer and Information Science, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-26630-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-26630-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26629-5
Online ISBN: 978-3-030-26630-1
eBook Packages: Computer ScienceComputer Science (R0)