Parametric and Non-parametric Mathematical Modelling Techniques: A Practical Approach of an Electrical Machine Identification

  • Oscar Gonzales Instituto Superior Tecnológico Sucre
Keywords: Parametric model, Non-parametric model, Experimental data, Modelling, Identification


Mathematical modeling is an important feature concerning the analysis and control of dynamic systems. Also, system identification is an approach for building mathematical expressions from experimental data taken from processes performance. In this context, the contemporaneous state of the art describes several modelling and identification techniques which are excellent alternatives to determine systems behavior through time. This paper presents a comprehensive review of the main techniques for modeling and identification from a parametric and no parametric perspective. Experimental data are taken from an electrical machine that is a DC motor from a didactic platform. The paper concludes with the analysis of results taken from different identification procedures.


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Almeida, M. P., Muñoz, M., de la Parra, I., & Perpiñán, O. (2017). Comparative study of PV power forecast using par-ametric and nonparametric PV models. Solar Energy, 155, 854-866.

Bermon, S., Metelkina, A., & Rendas, M. J. (2018, September). Comparison of parametric and non-parametric population modelling of sport performances. In 2018 26th European Sig-nal Processing Conference (EUSIPCO) (pp. 301-305). IEEE.

Bespalko, D. T., Amini, A., & Boumaiza, S. (2016, January). A high-order model looking beyond the first-order harmonic superposition assumption. In 2016 IEEE Topical Conference on Power Amplifiers for Wireless and Radio Applications (PAWR) (pp. 42-44). IEEE.

Chico, A., “Informe profesional,” Ph.D. dissertation, Escuela Superior Politécnica del LitoraL, 2015.

Cho, Y. U., & Kang, G. H. (2016, June). The force identification of 200kW IPMSM using phase reference spectrum. In 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific) (pp. 818-821). IEEE.

Choudhary, A., Baghel, A. S., & Sangwan, O. P. (2016, Janu-ary). Software reliability prediction modeling: A comparison of parametric and non-parametric modeling. In 2016 6th International Conference-Cloud System and Big Data En-gineering (Confluence) (pp. 649-653). IEEE.

Chu, Z., Sheng, C., Zhu, M., Chen, B., & Li, H. (2018). A robust adaptive identification of sinusoidal signal with unknown frequency. IEEE Transactions on Circuits and Systems II: Ex-press Briefs, 66(9), 1562-1566.

Deniz, F. N., Alagoz, B. B., & Tan, N. (2015, November). PID con-troller design based on second order model approximation by using stability boundary locus fitting. In 2015 9th Interna-tional Conference on Electrical and Electronics Engineering (ELECO) (pp. 827-831). IEEE.

Devadasu, G., & Sushama, M. (2016, February). A novel multi-ple fault identification with fast fourier transform analysis. In 2016 International Conference on Emerging Trends in En-gineering, Technology and Science (ICETETS) (pp. 1-5). IEEE.

Faifer, M., Ottoboni, R., Prioli, M., & Toscani, S. (2016). Simplified modeling and identification of nonlinear systems under quasi-sinusoidal conditions. IEEE Transactions on Instrumen-tation and Measurement, 65(6), 1508-1515.

Faisal, A., Nora, A., Seol, J., Renvall, H., & Salmelin, R. (2015, June). Kernel convolution model for decoding sounds from time-varying neural responses. In 2015 International Work-shop on Pattern Recognition in NeuroImaging (pp. 49-52). IEEE.

Feldman, A., Akbar, R., & Entekhabi, D. (2018, July). A First-Order Radiative Transfer Model for Global Soil Moisture Re-trievals Under Vegetation Canopies. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Sympo-sium (pp. 100-103). IEEE.

Gerling, D. (2016). Electrical Machines. Springer-Verlag Berlin An.

Gonzales, O., & Rosales, A. (2018, October). Sliding mode controller based on a linear quadratic integral regulator surface for power control on a dual active bridge converter. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.

Gonzales, O., Cela, A., & Herrera, M. (2017, October). Model predictive control tuning based on Extended Kalman Filter. In 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.

Herrera, M., Gonzales, O., Leica, P., & Camacho, O. (2018, October). Robust controller based on an optimal-integral surface for quadruple-tank process. In 2018 IEEE Third Ecua-dor Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.

Ishiyama, R., Takahashi, T., Makino, K., & Kudo, Y. (2018, No-vember). Fast Image Matching Based on Fourier-Mellin Phase Correlation for Tag-Less Identification of Mass-Produced Parts. In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 380-384). IEEE.

Ke, C., Huang, Q., Zhang, L., & Fang, Y. (2017, November). Modeling head-related impulse response based on adap-tive Fourier decomposition. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 3084-3088). IEEE.

Liu, J., Qin, X., Zhang, Q., Ding, X., & Zhan, P. (2019, May). Modal Frequency Identification of Quayside Container Crane Based on Empirical Mode Decomposition and Power Spectrum. In 2019 International Conference on Advances in Construction Machinery and Vehicle Engineering (ICACMVE) (pp. 322-327). IEEE.

Myint, L. M., & Tantaswadi, P. (2019, June). Noise Predictive Multi-Track Joint Viterbi Detector Using Infinite Impulse Re-sponse Filter in BPMR's Multi-Track Read Channel. In 2019 34th International Technical Conference on Cir-cuits/Systems, Computers and Communications (ITC-CSCC) (pp. 1-4). IEEE.

Oliveira, L., Bento, A., Leite, V., & Gomide, F. (2019, June). Robust evolving granular feedback linearization. In Interna-tional Fuzzy Systems Association World Congress (pp. 442-452). Springer, Cham.

Qianqian, L., Jingyuan, Z., & Bing, C. (2017, October). Study on life prediction of radar based on non-parametric regression model. In 2017 13th IEEE International Conference on Elec-tronic Measurement & Instruments (ICEMI) (pp. 586-590). IEEE.

Ribeiro, L. N., de Almeida, A. L., & Mota, J. C. (2015, December). Identification of separable systems using trilin-ear filtering. In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Pro-cessing (CAMSAP) (pp. 189-192). IEEE.

Rigatos, G. G. (2011). Modelling and control for intelligent industrial systems. adaptive algorithms in robotics and in-dustrial engineering.

Saghir, M., Naimi, Y., & Tahiri, M. (2018, April). First-order math-ematical modeling of biogas production: Application for the controlled landfill of fez. In 2018 Renewable Energies, Power Systems & Green Inclusive Economy (REPS-GIE) (pp. 1-6). IEEE.

Sanaullah, M., & Chowdhury, M. H. (2014, August). A new real pole delay model for RLC interconnect using second order approximation. In 2014 IEEE 57th International Midwest Sym-posium on Circuits and Systems (MWSCAS) (pp. 238-241). IEEE.

Sato, K. (2017). Riemannian optimal model reduction of linear second-order systems. IEEE control systems letters, 1(1), 2-7.

Schilders, W. H. A., & Lungten, S. (2018, August). Model order reduction for dynamic thermal models of LED packages. In 2018 IEEE MTT-S International Conference on Numerical Elec-tromagnetic and Multiphysics Modeling and Optimization (NEMO) (pp. 1-3). IEEE.

Seetharaman, P., & Rafii, Z. (2017, March). Cover song identi-fication with 2d fourier transform sequences. In 2017 IEEE In-ternational Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 616-620). IEEE.

Sirisantisamrid, K. (2017, October). Representation of Thai character features with impulse response of FIR system. In 2017 17th International Conference on Control, Automation and Systems (ICCAS) (pp. 517-521). IEEE.

Slavakis, K., Kopsinis, Y., & Theodoridis, S. (2011). Robust adap-tive sparse system identification by using weighted l 1 balls and Moreau envelopes. In 2011 19th European Signal Pro-cessing Conference (pp. 1924-1928). IEEE.

Snowden, T. J., van der Graaf, P. H., & Tindall, M. J. (2017). Methods of model reduction for large-scale biological sys-tems: a survey of current methods and trends. Bulletin of mathematical biology, 79(7), 1449-1486.

Ullrich, C. J., Birnbaum, D. M., & Bothsa, M. A. (2017). U.S. Pa-tent No. 9,547,366. Washington, DC: U.S. Patent and Trade-mark Office.

Volos, C. K., Pham, V. T., Vaidyanathan, S., Kyprianidis, I. M., & Stouboulos, I. N. (2016). The case of bidirectionally coupled nonlinear circuits via a memristor. In Advances and Appli-cations in Nonlinear Control Systems (pp. 317-350). Springer, Cham.

Xu, L. Y., Zhang, F., Kang, X. J., & Zhang, Y. S. (2015). Convolu-tion and correlation of nearest-neightbor model in algebra-ic signal processing.

Xue, W., Brookes, M., & Naylor, P. A. (2017, March). Frequency-domain under-modelled blind system identification based on cross power spectrum and sparsity regularization. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 591-595). IEEE.

You, W., Xu, F., & Limperopoulos, C. (2018, April). Linear convo-lution model of fetal circulation for hemodynamic respons-es to maternal hyperoxia using in utero functional MRI. In 2018 IEEE 15th International Symposium on Biomedical Im-aging (ISBI 2018) (pp. 1284-1287). IEEE.Pedregal, P. (2006). In-troduction to optimization (Vol. 46). Springer Science & Busi-ness Media.

Ahlstrom, M. L., Bartlett, D., Collier, C., Duchesne, J., Edelson, D., Gesino, A., & O'Sullivan, J. (2013). Knowledge is power: Efficiently integrating wind energy and wind forecasts. Power and Energy Magazine, IEEE, 11(6), 45-52.

Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M., and Draxl, C. (2011). The state of the art in short term prediction of wind power: A literature overview. Technical report, ANEMOS. Plus.

Tastu, J. (2013). Short-term wind power forecasting: probabilis-tic and space-time aspects.

Brown, B.G., R.W. Katz, and A.H. Murphy. (1984). Time series models to simulate and forecast wind speed and wind power. Journal of Climate and Applied Meteorology, 23, 1184-1195, DOI: 10.1175/1520-0450(1984)023<1184: TSMTSA>2.0.CO; 2.

Møller, J. K., Nielsen, H. A., & Madsen, H. (2008). Time-adaptive quantile regression. Computational Statistics & Data Analy-sis, 52(3), 1292-1303. 10.1016/j.csda.2007.06.027

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Gonzales, O. (2021). Parametric and Non-parametric Mathematical Modelling Techniques: A Practical Approach of an Electrical Machine Identification. Ecuadorian Science Journal, 5(1), 30-36.
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