In this paper, we propose a neural network-based modeling approach to achieve a model of an asynchronous machine. In addition, an adaptation is used for superior modeling estimation. The proposed approach is based on the use of multi-neural networks, so that the number of neural networks is set according to the learning error measurements of a single neural network. In addition, the effectiveness of a multi-neural network model has been verified by comparison with a single-neural network model. This new methodology offers a precise model that can be used in diagnostic, monitoring, and supervision systems.
Abdel OuahabGYahiaKNoureddineG (2017) Supervision of industrial manipulators using ANFIS. system ICCAS. In: International Conference on Control. Automation and Systems. Ramada Plaza. Korea: Jeju, 18–21.
2.
AchelaDFernandoKJayawardenaAW (1998) Runoff forecasting using RBF networks with OLS algorithm. Journal of Hydrologic Engineering3(3): 203–209.
3.
AiBAiYNiuL (2010) Study on the airgap flux density of the six-phase induction machine based on trapezoidal phase current waveform with CAD. International Forum on Information Technology and Applications1: 343–346.
4.
BandibasJCKohyamaK (2001) An efficient artificial neural network training method through induced learning retardation: inhibited brain learning. Asian Journal of Geoinformatics1(4): 45–55.
5.
CameronDKnealePSeeL (2002) An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment. Hydrological Processes16: 1033–1046.
6.
DaveyKR (1998) Analytic analysis of single- and three-phase induction motors. IEEE Transactions on Magnetics34(5): 3721–3727.
7.
FukudaTShibataT (1992) Theory and applications of neural networks for industrial control systems. IEEE Transactions on Industrial Electronics39(6): 472–489.
8.
GardnerMWDorlingSR (1998) Artificial neural networks. The multilayer perceptron. A review of applications in the atmospheric sciences. Atmospheric Environment32: 2627–2636.
9.
GhriebAOKourdYMessaoudiK, et al. (2020) Hardware implementation using xsg of new fault detection method applied to robot manipulator. Mechatronic Systems and Control48: 1–9.
10.
GlebovRSBondarenkoAG (2013) Modeling of the real-time control system. Industrial Automated Control Systems and Controllers7: 56–59.
11.
GuoSBaiC (2022) Coupling effect of unbalanced magnetic pull and ball bearing on nonlinear vibration of motor rotor system. Journal of Vibration and Control28(5–6): 665–676.
12.
IsmealGAKyslanKFedákV (2014) DC motor identification based on Recurrent Neural Networks. In: Proceedings of the 16th International Conference on Mechatronics. IEEE, 701–705.
13.
JalaliMHGeoff RideoutD (2022) Frequency-based decoupling and finite element model updating in vibration of cable–beam systems. Journal of Vibration and Control: Journal of Victorian Culture28(11–12): 1520–1535.
14.
JunYYunbingWGuoliangZ, et al. (2011) Simulation and research of asynchronous motor considering iron loss in Matlab /Simulink. Journal of Zhengzhou University of Light Industry26(1): 83–86.
15.
KadaBHebaliMBouguennaIF, et al. (2024) High-Performance Inverse Artificial Neural Network Controller for Asynchronous Motor Control. The Journal of Engineering and Exact Sciences10(9): 20857.
16.
KerdpiboonSKerrWLDevahastinS (2006) Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content. Food Research International39: 1110–1118.
17.
LenzenNAltayO (2024) Feedforward neural network-assisted parameter identification and tuning for uniaxial superelastic shape memory alloy models under dynamic loads. Journal of Vibration and Control31(1-2): 51–69.
18.
LinJ-WHuangC-WShihC-H, et al. (2011) Retracted: fuzzy lyapunov stability analysis and NN modeling for tension leg platform systems. Journal of Vibration and Control17(1): 151–158.
19.
O’RourkeCJQasimMMOverlinMR, et al. (2019) A geometric interpretation of reference frames and transformations: dq0, clarke, and park. IEEE Transactions on Energy Conversion4: 2070–2083.
20.
ParlosAGChongKTAtiyaAF (1994) Application of the recurrent multilayer perceptron in modelling complex process dynamics. IEEE Transactions on Neural Networks5: 255–266.
21.
RashidiMAlhuyi NazariMMahariqI, et al. (2022) Modeling and sensitivity analysis of thermal conductivity of ethylene glycol-water based nanofluids with alumina nanoparticles. Experimental Techniques16: 1–8.
SmolinVITopolskayaIG (2014) Amplitude control of the moment of a three-phase asynchronous drive based on the generalized EnergyFlow principles. Russian Electrical Engineering85(4): 205–209.
24.
TangDChenLTianZF, et al. (2018) Adaptive nonlinear optimal control for active suppression of airfoil flutter via a novel neural-network-based controller. Journal of Vibration and Control24(22): 5261–5272.
25.
TathawadekarNDoanNAKSilvaCF, et al. (2021) Modeling of the nonlinear flame response of a bunsen-type flame via multilayer perceptron. Proceedings of the Combustion Institute38: 6261–6269.
26.
TokarASMomciloM (2000) Precipitation–runoff modeling using artificial neural networks and conceptual models. Journal of Hydrologic Engineering5(20): 156–161.
27.
VahediAAliJ (2022) Constraint optimization of nonlinear McPherson suspension system using genetic algorithm and ADAMS software. Journal of Vibration and Control28(21–22): 3140–3151.
28.
VahediAJamaliA (2022) Constraint optimization of nonlinear McPherson suspension system using genetic algorithm and ADAMS software. Journal of Vibration and Control28(21–22): 31403151.
29.
WangXDingYShaoH (1998) The improved radial basis function neural network and its application. Artificial Life and Robotics2: 8–11.
30.
WangJChenXZhangF, et al. (2021) Building load forecasting using deep neural network with efficient feature fusion. Journal of Modern Power Systems and Clean Energy9: 160–169.
31.
WangLWuYPanX, et al. (2023) Speed feedback observation algorithm based on parameter introduction and its application in asynchronous motor. Proceedings of the Institution of Mechanical Engineers, Part I. Proceedings of the Institution of Mechanical Engineers - Part I: Journal of Systems & Control Engineering237(1): 58–71.
32.
YildizAB (2012) Electrical equivalent circuit based modeling and analysis of direct current motors. International Journal of Electrical Power & Energy Systems43: 1043–1047.
33.
ZhangBGovindarajuRS (2003) Geomorphology-based artificial neural networks (GANN) for estimation of direct runoff over watershed. Journal of Hydrology273: 18–34.
