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International Letters of Chemistry, Physics and Astronomy
ILCPA Volume 55
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Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System
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Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System

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Abstract:

This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.

Info:

Periodical:
International Letters of Chemistry, Physics and Astronomy (Volume 55)
Pages:
1-11
DOI:
https://doi.org/10.18052/www.scipress.com/ILCPA.55.1
Citation:
A. Rosyid et al., "Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System", International Letters of Chemistry, Physics and Astronomy, Vol. 55, pp. 1-11, 2015
Online since:
July 2015
Authors:
Abdur Rosyid, Mohanad Alata, Mohamed El Madany
Keywords:
Adaptive Neuro-Fuzzy Inference System (ANFIS), Linear Quadratic Regulator (LQR), Rotor-Bearing-Support System, Vibration Control
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Distribution:
Open Access

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References:

Filbo, E.L.V.C., et al. A fuzzy logic controller for the active control of rotor vibration using an active magnetic bearing as an actuator. in ABCM Symposium Series in Mechatronics. (2012).

Nagi, F.H., J.I. Inayat-Hussain, and S.K. Ahmed, Fuzzy bang-bang relay control of a singleaxis active magnetic bearing system. Simulation Modelling Practice and Theory, 2009. 17(10): pp.1734-1747.

Chen, K. -Y., et al., A self-tuning fuzzy PID-type controller design for unbalance compensation in an active magnetic bearing. Expert Systems with Applications, 2009. 36(4): pp.8560-8570.

Tung, P. -C., et al., Design of model-based unbalance compensator with fuzzy gain tuning mechanism for an active magnetic bearing system. Expert Systems with Applications, 2011. 38(10): pp.12861-12868.

Couzon, P.Y. and J. Der Hagopian, Neuro-fuzzy Active Control of Rotor Suspended on Active Magnetic Bearing. Journal of Vibration and Control, 2007. 13(4): pp.365-384.

Gong, X. and D. Cao, Fuzzy proportional-integral-derivative control of an overhang rotor with double discs based on the active tilting pad journal bearing. Journal of Vibration and Control, (2012).

Koroishi, E.H., V. Steffen, and J. Mahfoud, Fuzzy Control of Rotor System Using an Electromagnetic Actuator. MATEC Web of Conferences, 2012. 1: p.09003.

Borges, J.M., et al., Rotor-bearing vibration control system based on fuzzy controller and smart actuators. International Journal of Multiphysics, 2013. 7(3): pp.197-205.

Li, W., P. Maißer, and H. Enge, Self-learning control applied to vibration control of a rotating spindle by piezopusher bearings. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2004: p.218.

Lim, S., S. -M. Park, and K. -I. Kim, AI vibration control of high-speed rotor systems using electrorheological fluid. Journal of Sound and Vibration, 2005. 284(3-5): pp.685-703.

Tammi, K., J. Hatonen, and S. Daley, Novel Adaptive Repetitive Algorithm for Active Vibration Control of a Variable-Speed Rotor. Journal of Mechanical Science and Technology, 2007. 21: pp.855-859.

Tammi, K., Gradient-Based Repetitive Learning Control for Rotor Vibration Control. International Journal of Intelligent Control and Systems, 2008. 13: pp.222-232.

Buttini, T.M. and R. Nicoletti, Self-Identification Algorithm for the Autonomous Control of Lateral Vibration in Flexible Rotors. International Journal of Rotating Machinery, 2012. 2012: pp.1-13.

Rosyid, A., Vibration Intelligent Control of a Reduced-order Finite Element Model of a Rotor - Journal Bearing System. Mechanical Engineering Department, King Saud University, Riyadh, 2014. pp.9-54.

Jang, J. -S.R., ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics, 1993. 23(3).

Jang, J. -S.R., C. -T. Sun, and E. Mizutani, ANFIS: Adaptive Neuro-Fuzzy Inference Systems, in Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence1997, Prentice Hall: New Jersey, USA. pp.335-363.

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