Abstract
Unbalance vibration directly affects the operational precision, stability and life of rotary machinery. Profiting from the active control speciality of active magnetic bearing (AMB), unbalance vibration of rotor system with AMBs can be compensated and controlled automatically. This paper considers unbalance vibration minimum for rotor system with AMBs. Deep learning theory is utilized to design a compensation controller, which is added to the PID feedback control. The structure of the compensation controller is established by a deep neural network with 2 hidden layers, and its operation algorithms are designed. Model of a 4-DOF rigid rotor with AMBs is established for controller parameter setting and simulation. The unbalance vibration control of different controllers at fixed rotational speed is simulated, and the control effects of the proposed controller are demonstrated via unbalance vibration analysis and control current analysis. This research provides a new adaptive control approach for AMB control of unbalance minimum compensation, and it can also be applied in other multi-dimension vibration control.
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References
Schweitzer, G., Maslen, E.H.: Magnetic Bearings: Theory, Design, and Application to Rotating Machinery. Springer, Berlin (2009)
Chen, Q., Liu, G., Han, B.: Unbalance vibration suppression for AMBs system using adaptive notch filter. Mech. Syst. Signal Process. 93, 136–150 (2017)
Hui, C., Shi, L., Wang, J., Yu, S.: Adaptive unbalance vibration control of active magnetic bearing systems for the HTR-10GT. In: International Conference on Nuclear Engineering, pp. 793–801. ASME, Xi’an (2010)
He, Y., Shi, L., Shi, Z., Sun, Z.: Unbalance compensation of a full scale test rig designed for HTR-10GT: a frequency-domain approach based on iterative learning control. Science and Technology and Nuclear Installations, pp. 1–15 (2017)
Tung, P.C., Tsai, M.T., Chen, K.Y., Fan, Y.H., Chou, F.C.: Design of model-based unbalance compensator with fuzzy gain tuning mechanism for an active magnetic bearing system. Expert Syst. Appl. 38(10), 12861–12868 (2011)
Kuseyri, İ.S.: Robust control and unbalance compensation of rotor/active magnetic bearing systems. J. Vib. Control 18(6), 817–832 (2012)
Fang, J., Xu, X., Xie, J.: Active vibration control of rotor imbalance in active magnetic bearing systems. J. Vib. Control 21(4), 684–700 (2013)
Okubo, S., Nakamura, Y., Wakui, S.: Unbalance vibration control for active magnetic bearing using automatic balancing system and peak-of-gain control. In: IEEE International Conference on Mechatronics, vol. 307, pp. 105–110. IEEE, Vicenza (2013)
Heindel, S., Becker, F., Rinderknecht, S.: Unbalance and resonance elimination with active bearings on a Jeffcott rotor. Mech. Syst. Signal Process. 85, 339–353 (2017)
Qiao, X., Hu, G.: Active control for multinode unbalanced vibration of flexible spindle rotor system with active magnetic bearing. Shock Vib. 12, 1–9 (2017)
Saito, D., Waku, S.: Trial of applying unbalance vibration compensator to axial position of the rotor with active magnetic bearings. J. Jpn. Soc. Precis. Eng. 84(2), 210–208 (2018)
Cui, P.L., Zhao, G.Z., Fang, J.C., Li, H.T.: Adaptive control of unbalance vibration for magnetic bearings based on phase-shift notch filter within the whole frequency range. J. Vib. Shock 34(20), 16–20 (2015)
Jiang, K., Zhu, C., Chen, L.: Unbalance compensation by recursive seeking unbalance mass position in active magnetic bearing-rotor system. IEEE Trans. Ind. Electron. 62(9), 5655–5664 (2015)
Paul, M., Hofmann, W., Steffani, H.F.: Compensation for unbalances with aid of neural networks. In: Proceedings of the Sixth International Symposium on Magnetic Bearings, pp. 693–701. Massachussetts Institute of Technology (MIT), Cambridge MA (1998)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Punjani, A., Abbeel, P.: Deep learning helicopter dynamics models. In: IEEE International Conference on Robotics and Automation, pp. 3223–3230. IEEE, Seattle (2015)
Hung, J.Y.: Magnetic bearing control using fuzzy logic. IEEE Trans. Ind. Appl. 31(6), 1492–1497 (1995)
Dahl, G.E., Sainath, T.N., Hinton, G.E.: Improving deep neural networks for LVCSR using rectified linear units and dropout. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 26, pp. 8609–8613. IEEE, Vancouver (2013)
Sun, T., Pei, H., Pan, Y., Zhou, H., Zhang, C.: Neural network-based sliding mode adaptive control for robot manipulators. Neurocomputing 74(14–15), 2377–2384 (2011)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
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This work is supported by the National Natural Science Foundation of China (Grant No. 11772103).
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Yao, X., Chen, Z., Jiao, Y. (2019). Unbalance Vibration Compensation Control Using Deep Network for Rotor System with Active Magnetic Bearings. In: Cavalca, K., Weber, H. (eds) Proceedings of the 10th International Conference on Rotor Dynamics – IFToMM. IFToMM 2018. Mechanisms and Machine Science, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-319-99262-4_6
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DOI: https://doi.org/10.1007/978-3-319-99262-4_6
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