Restricted accessResearch articleFirst published online 2021-3
Electromagnetic torque control for six-phase induction motor expelled continuously variable transmission clumped system using sage dynamic control system
The continuously variable transmission (CVT) clumped system with lots of nonlinear uncertainties operated by the six-phase induction motor (SIM) is lacking in good control performance for using the linear control. In light of good ability of learning for nonlinear uncertainties, the sage dynamic control system using mixed modified recurrent Rogers–Szego polynomials neural network (MMRRSPNN) control and revised grey wolf optimization (RGWO) with two adjusted factors is proposed to acquire better control performance. The MMRRSPNN control and RGWO with two adjusted factors can execute intendant control, modified recurrent Rogers–Szego polynomials neural network (MRRSPNN) control with a fitted learning rule, and repay control with an evaluated rule. In addition, in the light of the Lyapunov stability theorem, the fitted learning rule in the MRRSPNN and the evaluated rule of the repay control are founded. Besides, the RGWO with two adjusted factors yields two changeable learning rates for two weights parameters to find two optimal values and to speed-up convergence of two weights parameters. Experimental results in comparisons with those control systems are demonstrated to confirm that the proposed control system can achieve better control performance.
TingJ.C. and ChenD.F., SynRM servo-drive CVT systems using MRRHPNN control with mend ACO, J. Power Electronics18(5) (2018), 1409–1423.
2.
MunozA.R. and LipoT.A., Dual stator winding induction machine drive, IEEE Trans. Industry Applications36(5) (2000), 1369–1379.
3.
OjoO. and DavidsonI.E., PWM-VSI inverter assisted stand-alone dual stator winding induction generator, IEEE Trans. Industry Applications36(6) (2000), 1604–1611.
4.
SinghG.K.NamK. and LimS.K., A simple indirect field-oriented control scheme for multiphase induction machine, IEEE Trans. Industrial Electronics52(4) (2005), 1177–1184.
5.
BojoiR.LazzariM.ProfumoF. and TenconiA., Digital field-oriented control for dual three-phase induction motor drives, IEEE Trans. Industry Applications39(3) (2003), 752–760.
6.
BrushE.F.KirtleyJ.L. and PetersD.T., Die-cast copper rotors as strategy for improving induction motor efficiency, in: Electrical Insulation Conference and Electrical Manufacturing Exhibition Organization, Nashville, Tennessee, USA, 2007, pp. 322–327.
7.
LinC.H., A six-phase CRIM driving CVT using blend modified recurrent Gegenbauer OPNN Control, J. Power Electronics16(4) (2016), 1438–1454.
8.
LinC.H., Modelling and control of six-phase induction motor servo-driven continuously variable transmission system using blend modified recurrent Gegenbauer orthogonal polynomial neural network control system and amended artificial bee colony optimization, Intl. J. Numerical Modelling: Electronic Networks, Devices and Fields29(5) (2016), 915–942.
9.
LinC.H. and HwangC.C., Multiobjective optimization design for a six-phase copper rotor induction motor mounted with a scroll compressor, IEEE Trans. Magnetics52(2016) 9401604.
10.
LinC.H., Blend modified recurrent Gegenbauer orthogonal polynomial neural network control for six-phase copper rotor induction motor servo-driven continuously variable transmission system using amended artificial bee colony optimization, Trans. Institute of Measurement and Control39(2017) 921–950.
11.
LinC.H., Comparative dynamic control for continuously variable transmission with nonlinear uncertainty using blend amend recurrent Gegenbauer-functional-expansions neural network, Nonlinear Dynamics87 (2017), 1467–1493.
12.
LinC.H., Comparative dynamic control of SynRM servodrive continuously variable transmission system using blend amend recurrent Gegenbauer-functional-expansions neural network control and altered artificial bee colony optimization, J. Dynamic Systems, Measurement, and Control139(5) (2017), 051007.
13.
LinC.H. and HwangC.C., Multi-objective optimization design using amended particle swarm optimization and Taguchi method for a six-phase copper rotor induction motor, Engineering Optimization49 (2017), 693–708.
14.
NouicerA.NouicerE. and FeliachiM., Neural network-DFT based model for magnetostrictive hysteresis, Intl. J. Applied Electromagnetics and Mechanics42(3) (2013), 343–348.
15.
HeR. and ZhengS., Fuzzy neural network multi-modal vibration control of thin cylindrical shells laminated with photostrictive actuators, Intl. J. Applied Electromagnetics and Mechanics46(4) (2014), 951–963.
16.
NagataF.HayashiS.NagatmiT.OtsukaA. and WatanabeK., Application of fuzzy reasoning and neural network to feed rate control of a machining robot, Intl. J. Applied Electromagnetics and Mechanics52(3–4) (2016), 897–905.
17.
TingJ.C. and ChenD.F., Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system, J. Mechanical Science and Technology32(9) (2018), 4399–4412.
18.
LinC.H., Composite recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization, Nonlinear Dynamics81(3) (2015), 1219–1245.
19.
TingJ.C. and ChenD.F., Nonlinear backstepping control of SynRM drive systems using reformed recurrent Hermite polynomial neural networks with adaptive law and error estimated law, J. Power Electronics8(5) (2018), 1380–1397.
20.
LinC.H. and ChangK.T., Admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system for synchronous reluctance motor driving continuously variable transmission system, Proc. IMechE. Part I: J. Systems and Control Engineering234(2) (2020), 183–198.
21.
SzegoG., Beitrag zur theorie der thetafunktionen, Sitz Preuss. Akad. Wiss. Phys. Math. Ki.19 (1926), 242–252.
22.
ValentinaE.B.HongJ.L.GuJ. and LinT.C., A novel recurrent neural network algorithm with long short-term memory model for futures trading, J. Intelligent and Fuzzy Systems37(4) (2019), 4477–4484.
23.
LinC.H. and ChangK.T., SCRIM drive system using adaptive backstepping control and mended recurrent Romanovski polynomials neural network with reformed particle swarm optimization, Intl. J. Adaptive Control and Signal Processing33(5) (2019), 802–828.
24.
LinC.H., Backstepping control and revamped recurrent fuzzy neural network with mended ant colony optimization applied in SCRIM drive system, J. Intelligent and Fuzzy Systems36(4) (2019), 3447–3459.
25.
EmaryE.YamanyW.HassanienA.E. and SnaselV., Multi-objective gray-wolf optimization for attribute reduction, Procedia Computer Science1(65) (2015), 623–632.
26.
MosaviM.KhisheM. and GhamgosarA., Classification of sonar data set using neural network trained by gray wolf optimization, Neural Network World26(4) (2016), 393–415.
27.
KhandelwalA.BhargavaA.SharmaA. and SharmaH., Modified grey wolf optimization algorithm for transmission network expansion planning problem, Arabian Journal for Science and Engineering43(6) (2018), 2899–2908.
28.
MirjaliliS.MirjaliliS.M. and LewisA., Grey wolf optimizer, Advances in Engineering Software69(3) (2014), 46–61.
29.
SultanaU.KhairuddinA.B.MokhtarA.S. and ZareenN., Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system, Energy111 (2016), 525–536.
30.
ParsianA.RamezaniM. and GhadimiN., A hybrid neural network-gray wolf optimization algorithm for melanoma detection, Biomedical Research28(8) (2017), 3408–3411.
31.
DuangjaiJ. and PongsakP., Grey wolf algorithm with borda count for feature selection in classification, in: 3rd Intl. Conf Control and Robotics Engineering (ICCRE), Nagoya, Japan, 2018, pp. 238–242.
32.
HongC.W. and ChenC.C., Dynamic performance simulation of a continuously variable transmission motorcycle for fuzzy autopilot design, Proc IMechE Part D: J. Automobile Engineering211(6) (1997), 477–490.
33.
SrivastavaN. and HaqueI., Transient dynamics of metal V-belt CVT: Effects of bandpack slip and friction characteristic, Mechanism and Machine Theory43(4) (2008), 457–479.
34.
SrivastavaN. and HaqueI., A review on belt and chain continuously variable transmissions (CVT): Dynamics and control, Mechanism and Machine Theory44(1) (2009), 19–41.
35.
HuJ.GuoZ.PengH. and ZhengD., Research on regenerative braking control strategy of plug-in hybrid electric vehicle considering CVT ratio rate of change, Proc IMechE Part D: J. Automobile Engineering232(14) (2017), 1931–1943.
36.
LinC.H. and ChangK.T., SynRM Driving CVT System Using an ARGOPNN with MPSO control system, J. Power Electronics19(3) (2019), 771–783.
37.
LinC.H., Dynamic control for permanent magnet synchronous generator system using novel modified recurrent wavelet neural network, Nonlinear Dynamics77(4) (2014), 1261–1284.
38.
LinC.H., Permanent magnet synchronous motor controlled V-belt continuously variable transmission driven electric scooter using hybrid modified recurrent Legendre NN control system, Intl. J. Applied Electromagnetics and Mechanics47(1) (2015), 211–235.
39.
LinC.H. and ChangK.T., Electromagnetic torque control for synchronous reluctance motor servo-drive system applied in continuously variable transmission system, Intl. J. Applied Electromagnetics and Mechanics62(2) (2020), 355–382.
40.
AstromK.J. and HagglundT., PID Controller: Theory, Design, and Tuning, Instrument Society of America, Research Triangle Park, North Carolina, USA, 1995.
41.
HagglundT. and AstromK.J., Revisiting the Ziegler–Nichols tuning rules for PI control, Asian J. Control4(4) (2002), 364–380.
42.
HagglundT. and AstromK.J., Revisiting the Ziegler–Nichols tuning rules for PI control-part II: the frequency response method, Asian J. Control6(4) (2004), 469–482.
43.
GasperG. and RahmanM., Encyclopedia of Mathematics and its Applications, 2nd edn, Cambridge University Press, 2004.
44.
AstromK.J. and WittenmarkB., Adaptive Control, Addison-Wesley, New York, 1995.
45.
SlotineJ.J.E. and LiW., Applied Nonlinear Control, Prentice Hall, Englewood Cliffs, New Jersey, 1991.
