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Dual-mode predictive control of a rotor suspension system

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Abstract

Rotor active magnetic bearing (rotor-AMB) systems are frequently used to alleviate vibrations for various applications such as in national defense, manufacturing industries, IC production, and aerospace engineering. One obstacle to improve machining efficiency and quality is the open-loop instability of rotor-AMB systems during the machining process. We built a closed-loop processing platform using a spindle rotor installed with AMBs and thereby developed a rotor-AMB suspension system embedded with a dual-mode predictive controller (DMPC). The performance of the system is thus substantially improved. In the proposed DMPC, both model-based prediction and receding horizon optimization are utilized to guarantee the closed-loop stability of the rotor-AMB suspension systems with input constraints. Finally, the effectiveness and superiority of the proposed method are examined through substantial levitation experiments on a machining platform with installed AMBs.

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References

  1. 1

    Knospe C R. Active magnetic bearings for machining applications. Control Eng Pract, 2007, 15: 307–313

  2. 2

    Lim T M, Zhang D, Yang J, et al. Design and parameter estimation of hybrid magnetic bearings for blood pump applications. Mech Syst Signal Process, 2009, 23: 2352–2382

  3. 3

    Fittro R L, Knospe C R. μ control of a high speed spindle thrust magnetic bearing. In: Proceedings of IEEE International Conference on Control Applications, 1999. 570–575

  4. 4

    Zhang L, Liu K. Riccati difference equation in optimal control for magnetic bearings. Sci China Technol Sci, 2012, 55: 2107–2114

  5. 5

    Schweitzer G, Maslen E H. Magnetic Bearings: Theory, Design, and Application to Rotating Machinery. Berlin: Springer, 2009

  6. 6

    Maslen E H, Sawicki J T. μ-synthesis for magnetic bearings: why use such a complicated tool? In: Proceedings of ASME 2007 International Mechanical Engineering Congress and Exposition, 2007. 1103–1112

  7. 7

    Sivrioglu S. Adaptive control of nonlinear zero-bias current magnetic bearing system. Nonlinear Dyn, 2007, 48: 175–184

  8. 8

    Kang M S, Lyou J, Lee J K. Sliding mode control for an active magnetic bearing system subject to base motion. Mechatronics, 2010, 20: 171–178

  9. 9

    Kuseyri I S. Robust control and unbalance compensation of rotor/active magnetic bearing systems. J Vib Control, 2012, 18: 817–832

  10. 10

    Dong L L, You S L. Adaptive control of an active magnetic bearing with external disturbance. ISA Trans, 2014, 53: 1410–1419

  11. 11

    Pesch A H, Smirnov A, Pyrhonen O, et al. Magnetic bearing spindle tool tracking through μ-synthesis robust control. IEEE/ASME Trans Mechatron, 2015, 20: 1448–1457

  12. 12

    Kandil M S, Dubois M R, Bakay L S, et al. Application of second-order sliding-mode concepts to active magnetic bearings. IEEE Trans Ind Electron, 2018, 65: 855–864

  13. 13

    Xie J, Zeng X J, Zhang M M, et al. Implementation of active magnetic control system for piston centering in labyrinth piston compressor. Mechatronics, 2018, 54: 52–67

  14. 14

    Chen M, Wu Q X, Jiang C S, et al. Guaranteed transient performance based control with input saturation for near space vehicles. Sci China Inf Sci, 2014, 57: 052204

  15. 15

    Chen M, Ren B B, Wu Q X, et al. Anti-disturbance control of hypersonic flight vehicles with input saturation using disturbance observer. Sci China Inf Sci, 2015, 58: 070202

  16. 16

    Albin T. Benefits of model predictive control for gasoline airpath control. Sci China Inf Sci, 2018, 61: 070204

  17. 17

    Harinath E, Foguth L C, Braatz R D. A robust dual-mode MPC approach to ensuring critical quality attributes in quality-by-design. In: Proceedings of American Control Conference (ACC), 2016. 2041–2046

  18. 18

    Yuan Y, Zhang H T, Wu Y, et al. Bayesian learning-based model-predictive vibration control for thin-walled workpiece machining processes. IEEE/ASME Trans Mechatron, 2017, 22: 509–520

  19. 19

    Cai Z, Haq A A U, Cholette M E, et al. Energy efficiency and tracking performance evaluation for dual-mode model predictive control of HVAC systems. J Thermal Sci Eng Appl, 2018, 10: 041023

  20. 20

    Maciejowski J M. Predictive Control: With Constraints. Englewood Cliffs: Prentice Hall, 2002

  21. 21

    Ruan X G, Hou X Y, Ma H Y. Stability analysis of constrained MPC with CLF applied to discrete-time nonlinear system. Sci China Inf Sci, 2014, 57: 112201

  22. 22

    Zhang Y Y, Cao W H, Jin Y L, et al. An ensemble model based on weighted support vector regression and its application in annealing heating process. Sci China Inf Sci, 2019, 62: 049202

  23. 23

    Mayne D Q, Rawlings J B, Rao C V, et al. Constrained model predictive control: stability and optimality. Automatica, 2000, 36: 789–814

  24. 24

    Guo L L, Gao B Z, Li Y, et al. A fast algorithm for nonlinear model predictive control applied to HEV energy management systems. Sci China Inf Sci, 2017, 60: 092201

  25. 25

    Zhang H, Wang T, Zhao Y L. FIR system identification with set-valued and precise observations from multiple sensors. Sci China Inf Sci, 2019, 62: 052203

  26. 26

    Zhang H T, Wu Y, He D, et al. Model predictive control to mitigate chatters in milling processes with input constraints. Int J Mach Tools Manu, 2015, 91: 54–61

  27. 27

    Kouvaritakis B, Rossiter J A, Schuurmans J. Efficient robust predictive control. IEEE Trans Autom Control, 2000, 45: 1545–1549

  28. 28

    Zhang H T, Li H X, Chen G. Dual-mode predictive control algorithm for constrained Hammerstein systems. Int J Control, 2008, 81: 1609–1625

  29. 29

    Khalil H K. Nonlinear Systems. Englewood Cliffs: Prentice Hall, 2012

  30. 30

    Zhang H T, Chen G, Chen M Z Q. A novel dual-mode predictive control strategy for constrained Wiener systems. Int J Robust Nonlinear Control, 2009, 20: 975–986

  31. 31

    Zhang F Z. The Schur Complement and Its Applications. Berlin: Springer, 2006

  32. 32

    Lofberg J. YALMIP: a toolbox for modeling and optimization in matlab. In: Proceedings of IEEE International Conference on Robotics and Automation, 2004. 3753–3757

  33. 33

    Blondel V D, Boyd S P, Kimura H. Recent Advances in Learning and Control. Berlin: Springer, 2008

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. U1713203, 61803168, 51729501) and China Postdoctoral Science Foundation (Grant No. 2018M642822).

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Correspondence to Hai-Tao Zhang.

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Cite this article

Wu, Y., Ren, G. & Zhang, H. Dual-mode predictive control of a rotor suspension system. Sci. China Inf. Sci. 63, 112204 (2020). https://doi.org/10.1007/s11432-019-9896-2

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Keywords

  • dual-mode predictive control
  • system identification
  • optimization control
  • model predictive control
  • suspension control