A Nonlinear Flux Linkage Model for Bearingless Induction Motor Based on GWO-LSSVM
Open accessAbstract
The flux-linkage characteristics of bearingless induction motors (BIMs) are nonlinear, and the models established by the general analytical method cannot accurately reflect the actual characteristics of BIMs. Thus, a novel method for nonlinear modeling of BIM flux linkage is proposed in this paper. The main objective of this method is to improve the accuracy of the flux linkage model based on the least square support vector machine (LSSVM) technique by applying the gray wolf optimization (GWO) algorithm to determine the optimal kernel parameter and regularization parameter of the LSSVM automatically. In this method, all BIMs flux linkage data are obtained from the finite-element method. In this paper, the relationship between input and output of the nonlinear flux linkage model is studied, and the precision model of GWO-LSSVM flux linkage is obtained. The simulation results demonstrate that the GWO-LSSVM model has high prediction accuracy and strong prediction ability. In addition, the GWO-LSSVM model is compared with other models. From this simulation comparison, it can be concluded that GWO-LSSVM modeling has the characteristics of higher accuracy.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
GWO has advantages over PSO, ALO, MFO, SCA, WOA and MVO in global search ability, solution accuracy and convergence speed.
“Compared with some proposed intelligent optimization algorithms, such as PSO, ALO, moth-flame optimization (MFO), SCA, WOA and multi-verse optimizer (MVO), GWO has advantages in global search ability, solution accuracy and convergence speed [21].”
From Support vector regression-based operational effectiveness evaluation approach to reconnaissance satellite system (2023) · cites this paper as [21]
The Introduction describes GWO as having strong global search ability and says it outperforms GA and PSO in convergence speed and solution accuracy. It does not establish those advantages over ALO, MFO, SCA, WOA, or MVO; the paper’s reported comparisons instead involve ABC and SA. (LLM, conf=0.90)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 20,065 · see citing works
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works
Practical selection of SVM parameters and noise estimation for SVM regression
Cherkassky, V., & Ma, Y. (2003) · Neural Networks · doi:10.1016/s0893-6080(03)00169-2 · Cited by 2,034 · see citing works
Optimal control by least squares support vector machines
Suykens, J. A. K., Vandewalle, J. P. L., & Moor, B. D. (2001) · Neural Networks · doi:10.1016/s0893-6080(00)00077-0 · Cited by 541 · see citing works
Efficient SVM Regression Training with SMO
Flake, G. W., & Lawrence, S. (2002) · Machine Learning · doi:10.1023/a:1012474916001 · Cited by 317 · see citing works
Precup, R., David, R., & Petriu, E. M. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2607698 · Cited by 275 · see citing works
Overview of Bearingless Permanent-Magnet Synchronous Motors
Sun, X., Chen, L., & Yang, Z. (2012) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2232253 · Cited by 268 · see citing works
Self-citationSun, X., Chen, L., Yang, Z., et al. (2012) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2012.2202123 · Cited by 242 · see citing works
Self-citationSun, X., Chen, L., Jiang, H., et al. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2530040 · Cited by 237 · see citing works
Self-citationSalahshoor, K., Kordestani, M., & Khoshro, M. S. (2010) · Energy · doi:10.1016/j.energy.2010.06.001 · Cited by 236 · see citing works
Ibrahim, R. A., Elaziz, M. A., & Lu, S. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.04.028 · Cited by 216 · see citing works
A novel hybrid PSO–GWO approach for unit commitment problem
Kamboj, V. K. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-1962-4 · Cited by 212 · see citing works
Sun, X., Shi, Z., Chen, L., et al. (2016) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2016.2591925 · Cited by 200 · see citing works
Self-citationGrey wolf optimization based clustering algorithm for vehicular ad-hoc networks
Fahad, M., Aadil, F., Rehman, Z. U., et al. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.01.002 · Cited by 199 · see citing works
Sun, X., Shen, Y., Wang, S., et al. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2018.2803148 · Cited by 146 · see citing works
Self-citationImproved GWO algorithm for optimal design of truss structures
Kaveh, A., & Zakian, P. (2017) · Engineering With Computers · doi:10.1007/s00366-017-0567-1 · Cited by 145 · see citing works
Torque Analysis and Dynamic Performance Improvement of a PMSM for EVs by Skew Angle Optimization
Shi, Z., Sun, X., Cai, Y., et al. (2018) · IEEE Transactions on Applied Superconductivity · doi:10.1109/tasc.2018.2882419 · Cited by 144 · see citing works
Self-citationManohar, M., & Das, S. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2714675 · Cited by 136 · see citing works
Least squares recursive projection twin support vector machine for classification
Shao, Y., Deng, N.-Y., & Yang, Z. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.11.028 · Cited by 134 · see citing works
Self-citationSun, X., Su, B., Chen, L., et al. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2016.11.022 · Cited by 120 · see citing works
Self-citationDevelopment a new power management strategy for power split hybrid electric vehicles
Montazeri‐Gh, M., & Mahmoodi-k, M. (2015) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2015.04.024 · Cited by 119 · see citing works
Sun, X., Su, B., Wang, S., et al. (2018) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2018.2827103 · Cited by 108 · see citing works
Self-citationSystem Reliability Optimization Using Gray Wolf Optimizer Algorithm
Kumar, A., Pant, S., & Ram, M. (2016) · Quality and Reliability Engineering International · doi:10.1002/qre.2107 · Cited by 107 · see citing works
Kim, J., & Kim, R.-Y. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2821632 · Cited by 83 · see citing works
Sun, X., Jin, Z., Wang, S., et al. (2019) · IEEE Transactions on Applied Superconductivity · doi:10.1109/tasc.2019.2893295 · Cited by 83 · see citing works
Self-citationMultidisciplinary Design of Ultra-High-Speed Electrical Machines
Ismagilov, F. R., Uzhegov, N., Vavilov, V. E., et al. (2018) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2018.2803146 · Cited by 72 · see citing works
Yang, Z., Zhang, D., Sun, X., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2851590 · Cited by 68 · see citing works
Self-citationWei, Y., Ni, N., Liu, D., et al. (2017) · Mathematical Problems in Engineering · doi:10.1155/2017/9316713 · Cited by 66 · see citing works
Huang, J. E., Li, B., Jiang, H., et al. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2013.2279371 · Cited by 65 · see citing works
VRheab: a fully immersive motor rehabilitation system based on recurrent neural network
Avola, D., Cinque, L., Foresti, G. L., et al. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5730-1 · Cited by 52 · see citing works
Sun, X., Xue, Z., Zhu, J. G., et al. (2016) · IEEE Transactions on Applied Superconductivity · doi:10.1109/tasc.2016.2599708 · Cited by 47 · see citing works
Self-citationSinervo, A., & Arkkio, A. (2013) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2013.2291224 · Cited by 46 · see citing works
Design and Analysis of a Novel Bearingless Motor for a Miniature Axial Flow Blood Pump
Zad, H. S., Khan, T. I., & Lazoğlu, İ. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2762626 · Cited by 42 · see citing works
Bian, X., Zhang, Q., Zhang, L., et al. (2017) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2017.05.008 · Cited by 38 · see citing works
Improvements on least squares twin multi-class classification support vector machine
de Lima, M. D., da Costa, N. L., & Barbosa, R. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.06.040 · Cited by 38 · see citing works
Bu, W., Zhang, X., & He, F. (2018) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.22663 · Cited by 34 · see citing works
Sun, X., Su, B., Chen, L., et al. (2016) · International Journal of Applied Electromagnetics and Mechanics · doi:10.3233/jae-150165 · Cited by 33 · see citing works
Self-citationDesign and Optimization of a Radial Magnetic Bearing for High-Speed Motor With Flexible Rotor
Le, Y., Fang, J., & Wang, K. (2014) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2014.2386298 · Cited by 30 · see citing works
Implementation of speed controller for rotary hydraulic motor based on LS-SVM
Khalil, H. M., & El-Bardini, M. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.238 · Cited by 28 · see citing works
Sala, G., Valente, G., Formentini, A., et al. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2795590 · Cited by 28 · see citing works
1 kW/60,000 min −1 bearingless PM motor with combined winding for torque and rotor suspension
Dietz, D., Messager, G., & Binder, A. (2018) · IET Electric Power Applications · doi:10.1049/iet-epa.2018.0013 · Cited by 26 · see citing works
Analytical Polynomial Models of Nonlinear Magnetic Flux Linkage for SRM
Chen, H., Yan, W., Chen, L., et al. (2018) · IEEE Transactions on Applied Superconductivity · doi:10.1109/tasc.2018.2806913 · Cited by 16 · see citing works
Bu, W., Li, Z., Lu, C., et al. (2015) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331215617524 · Cited by 12 · see citing works
Magnetic suspension force in an induction bearingless motor with a squirrel cage rotor
Katou, T., Chibá, A., & Fukao, T. (2007) · Electrical Engineering in Japan · doi:10.1002/eej.20340 · Cited by 7 · see citing works
A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling
Borujeni, M. S., Ghaderi‐Zefrehei, M., Ghanegolmohammadi, F., et al. (2018) · Iranian Journal of Biotechnology · doi:10.21859/ijb.1542 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Han, C., Xiong, W., Xiong, M., & Liu, Z. (2023) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2023.000020 · Cited by 7 · see citing works
- Authors
- Li, K., Cheng, G., Sun, X., & Yang, Z.
- Affiliations
- Jiangsu University (CN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 36558-36567
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2921734430
- Cited by
- 30
- References
- 45
- Citations by year
- 2026: 1 · 2024: 5 · 2023: 3 · 2022: 1 · 2021: 7 · 2020: 8 · 2019: 5
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (51875261, 51475214)
- Natural Science Foundation of Jiangsu Province (BK20180046, BK20170071)
- Qinglan Project of Jiangsu Province
- Key Project of Natural Science Foundation of Jiangsu Higher Education Institutions (17KJA460005)
- Six Categories Talent Peak of Jiangsu Province (2015-XNYQC-003, 2016-GDZB-096)
- 333 Project of Jiangsu Province (BRA2017441)
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Motor Design and Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Sensorless Control of Electric Motors
- Physical Sciences › Engineering › Control and Systems Engineering › Magnetic Bearings and Levitation Dynamics
- Keywords
- bearingless induction motor, nonlinear models, grey wolf optimizer, least squares support vector machine, kernel parameter optimization, regularization parameter optimization, finite element simulation data, motor parameter identification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
