In view of the problems that the analytical inverse system decoupling method of bearingless induction motor is sensitive to the change of motor parameters and is greatly affected by unmodeled dynamics, and that of traditional proportional–derivative controller lacking the self-adaptive regulation ability, a neural network inverse system decoupling fuzzy self-tuning proportional–derivative control strategy is proposed for a bearingless induction motor system. First, under the conditions of considering the stator current dynamic of torque winding, and by neural network inverse system method, the bearingless induction motor system is decoupled into four pseudo-linear integral subsystems. Second, the traditional proportional–derivative controller is improved, and the fuzzy control algorithm is used to adjust the parameters of improved proportional–derivative controller adaptively. Thus, a neural network inverse system decoupling fuzzy self-tuning proportional–derivative control system is constructed for a bearingless induction motor. The simulation experimental results show that the proposed control strategy not only can effectively improve the stability, robustness and steady-state control accuracy of bearingless induction motor system, but also can significantly improve the dynamic response speed and the ability to resist the influences of motor parameter change and load disturbance.
ChibaA.Magnetic bearings and bearingless drives. Oxford: Butterworth-Heinemann, 2005.
2.
DingHFZhuHQHuaYZ.Optimization design of bearingless synchronous reluctance motor. IEEE T Appl Superconduct2018; 28(3): 5202905.
3.
LapotreBTakorabetNMeilbody-TabarF, et al. Radial force modeling for bearingless motors based on analyze of field origins. Proc IMechE, Part I: J Systems and Control Engineering2016; 230(4): 311–319.
4.
BuWSHeFZLuCX, et al. Unbalanced vibration control strategy of bearingless induction motor based on inverse system decoupling. Int J Appl Electromagnet Mech2016; 51(4): 455–469.
5.
MurakamiIMoriHShimizuM, et al. Study on rigidity of the superconducting magnetic bearing. Int J Appl Electromagnet Mech2019; 59(1): 191–200.
6.
SunXDSuBKChenL, et al. Precise control of a four degree-of-freedom permanent magnet biased active magnetic bearing system in a magnetically suspended direct-driven spindle using neural network inverse scheme. Mech Syst Signal Pr2017; 88: 36–48.
7.
HedlundMAbrahamssonJPerez-LoyaJJ, et al. Eddy currents in a passive magnetic axial thrust bearing for a flywheel energy storage system. Int J Appl Electromag Mech2017; 54(3): 389–404.
8.
YangZBWangKSunXD, et al. Load disturbance rejection control of a bearingless induction motor based on fractional-order integral sliding mode. Proc IMechE, Part I: J Systems and Control Engineering2018; 232(10): 1356–1364.
9.
BuWSZhangXFQiaoYK, et al. Equivalent two-phase mutual inductance model and measurement algorithm of three-phase bearingless motor. Trans Inst Meas Control2018; 40(2): 630–639.
10.
YangZBChenXSunXD, et al. Rotor radial disturbance control for a bearingless induction motor based on improved active disturbance rejection control. Int J Comput Math Electr Electron Eng2019; 38(1): 138–152.
11.
SunXDChenLJiangHB, et al. High-performance control for a bearingless permanent magnet synchronous motor using neural network inverse scheme plus internal model controllers. IEEE T Ind Electron2016; 63(6): 3479–3488.
12.
GruberWBauerWRadmanK.Comparison of homopolar and heteropolar bearingless reluctance slice motor prototypes. Proc IMechE, Part I: J Systems and Control Engineering2017; 231(5): 339SI–347.
13.
BuWSChenYPZuCL.Stator flux orientation inverse system decoupling control strategy of bearingless induction motor considering stator current dynamics. IEEJ Trans Electr Electron Eng2019; 14(4): 640–647.
14.
TsunodaWChibaAShinshiT.Vibration control for a rotor supported by oil-film bearings using a bearingless motor. IEEE-ASME T Mech2019; 24(3): 1368–1375.
15.
SunXDJinZJWangSH, et al. Performance improvement of torque and suspension force for a novel five-phase BFSPM machine for flywheel energy storage systems. IEEE T Appl Super Conduct2019; 29(2): 0601505.
16.
SeversonEL. Bearingless motor technology for industrial and transportation applications. In: IEEE transportation and electrification conference and expo (ITEC), Long Beach, CA, 13–15 June 2018, pp.266–273. New York: IEEE.
17.
SunXDSuBKWangSH, et al. Performance analysis of suspension force and torque in an IBPMSM with V-shape PMs for flywheel batteries. IEEE T Magnet2018; 54(11): 18196531.
18.
ZadHSKhanTILazogluI.Design and analysis of a novel bearingless motor for a miniature axial flow blood pump. IEEE T Ind Electron2018; 65(5): 4006–4016.
19.
SunXDChenLYangZB, et al. Speed-sensorless vector control of a bearingless induction motor with artificial neural network inverse speed observer. IEEE/ASME T Mechatron2013; 18(4): 1357–1366.
20.
BuWSLiZY.LS-SVM inverse system decoupling control strategy of bearingless induction motor considering stator current dynamics. IEEE Access2019; 7: 132130–132139.
21.
YangZBJiJLSunXD, et al. Active disturbance rejection control for bearingless induction motor based on hyperbolic tangent tracking differentiator. IEEE J Emerg Select Topic Power Electr2020; 8: 2623–2633.
22.
YangZBDingQFSunXD, et al. Design and analysis of a novel wound rotor for a bearingless induction motor. Int J Electr2019; 106(12): 1829–1844.
23.
BuWSLiBHeFZ, et al. Inverse system decoupling sliding mode control strategy of BLIM considering current dynamics. Int J Appl Electromag Mech2019; 60(1): 63–78.
24.
WangZQLiuXX.Nonlinear internal model control for bearingless induction motor based on neural network inversion. Acta Automat Sinica2013; 39(4): 433–439.
25.
BuWSHeFZLiZY, et al. Neural network inverse system decoupling control strategy of BLIM considering stator current dynamics. Trans Inst Meas Control2019; 41(3): 621–630.
26.
YaoJYJiaoZXMaDW.Adaptive robust control of DC motors with extended state observer. IEEE T Ind Electron2014; 61(7): 3630–3637.
27.
LinC.Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system. Trans Inst Meas Control2019; 41(14): 4114–4128.
28.
El-SousyFFMEl-NaggarMFAminM, et al. Robust adaptive neural-network backstepping control design for high-speed permanent-magnet synchronous motor drives: theory and experiments. IEEE Access2019; 7: 99327–99348.
29.
YangZBZhangDSunXD, et al. Adaptive exponential sliding mode control for a bearingless induction motor based on a disturbance observer. IEEE Access2018; 6: 35425–35434.
30.
BuWSTuXWLuCX, et al. Adaptive feedforward vibration compensation control strategy of bearingless induction motor. Int J Appl Electromag Mech2020; 63(2): 199–215.
31.
YangZBWangMTSunXD.Control system of bearingless induction motors based on adaptive neuro-fuzzy inference system. Trans Chin Soc Agr Eng2014; 30(2): 78–86.
32.
ZhuHQDuW.Decoupling control of bearingless permanent magnet synchronous motor based on inverse system using the adaptive neural-fuzzy inference system. Proc CSEE2019; 39(4): 1190–1197.
