Sentsov E.V., Meshcheryakov V.N. Neural Network Speed Observer Development to Improve the Dynamic Stability of a Sensorless Vector Control System

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Abstract

The paper considers the problem of the speed observer indication influence on the vector control system of an asynchronous motor. The classification of applied sensorless control methods is given. The analysis of advantages and disadvantages of the most common structures for constructing velocity observers is carried out. Mathematical models of individual observers were developed and created in the Matlab Simulink software package. The work of the obtained models was tested in various modes, data on the drive dynamics were obtained. Conclusions are drawn about the work of observers based on the classical mathematical apparatus in situations associated with the change in any parameters of the controlled object. The concept of neural networks is considered as a tool capable of leveling the shortcomings of classical observers. The analysis of neurostructures suitable for control tasks of complex dynamic objects has been carried out. An element of the Neural Network Toolbox (Deep Learning Toolbox) library, Predictive Controller, was used as a neuroregulator. A model was built using the neurostructure as an observer. The process of data integration and adjustment of neural network parameters is described in detail. A study of the obtained control system behavior in dynamic modes was carried out. Also, the vector control system behavior, an adaptation mechanism was developed that takes into account the advantages and disadvantages of 2 different approaches to the implementation of velocity determination to create a model with a combined observer structure. A model with a combined observer structure based on a neural network and a classical observer is obtained. The behavior of the resulting control system in various simulation modes has been studied.

Keywords

simulation, variable speed drive, control system, vector control, asynchronous motor, Predictive controller, observers, sensorless vector control, learning, neural networks.

Evgeny V. Sentsov Postgraduate Student, Electric Drive Department, Lipetsk State Technical University, Lipetsk, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0002-8383-5139

Victor N. Meshcheryakov D.Sc. (Engineering), Professor, Head of the Electric Drive Department, Electric Drive Department, Lipetsk State Technical University, Lipetsk, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0003-0984-5133

 
  1. Sentsov E.V., Meshcheryakov V.N. Development of an adaptive vector control velocity observer Primenenie i razvitie neyrosetevogo modelirovaniya dlya resheniya fundamental'nykh zadach v nauke i tekhnike: materialy Mezhdunarodnoy molodezhnoy nauchno-prakticheskoy konferentsii [International Youth Scientific and Practical Conference "Application and development of neural network modeling for solving fundamental problems in science and technology"]. Novocherkassk, Lik LLC Publ., 2018, pp. 36-47. (in Russian)
  2. Sokolovsky G.G. Elektroprivody peremennogo toka s chastotnym regulirovaniem [AC drives with frequency regu-lation]. Moscow, Academy Publ., 2006. 272 p. (In Russian)
  3. Novikov V.A., Savva S.V., Tatarintsev N.I. Elektroprivod v sovremennykh tekhnologiyakh [Electric drive in modern tech-nologies]. Moscow, Academy Publ., 2014. 400 p. (In Russian)
  4. Kalachev Ju.N. Vektornoe regulirovanie (zametki praktika) [Vector regulation (practice notes)]. Moscow, JeFO Publ., 2013. 63 p. (in Russian)
  5. Anuchin A.S. Sistemy upravlenijaj elektroprivodov [Electric drive control systems]. Moscow, MPEI Publishing House, 2015. 373 p. (in Russian)
  6. Kalachev Ju.N. Nabljudateli sostojanija v vektornom elek-troprivode (zapiski diletanta) [State observers in a vector elec-tric drive (amateur notes)]. Moscow, 2015. 60 p. (in Russian)
  7. Vinogradov A.B. Vektornoeupravleniejelektroprivoda-mi peremennogotoka [Vector control of AC drives]. Ivanovo, Ivanovo State Power Engineering University named after V.I. Lenin Publ., 2008. 98 p. (in Russian)
  8. Terehov V.M., Osipov O.I. Sistemy upravlenijaj elektroprivodov [Electric drive control systems]. Moscow, Publishing Center "Academy", 2005. 304 p. (in Russian)
  9. Isakov A.C. Implementation of the state observer for an in-duction motor with a squirrel-cage rotor in a sensorless vector control system. Nauchno-tekhnicheskiy vestnik SPbGU ITMO [Scientific and technical bulletin. St. Petersburg State University ITMO], 2007, no. 38, pp. 280-286. (in Russian)
  10. Usoltsev A.A. Chastotnoe upravlenie asinhronnymi dvigateljami [Frequency control of asynchronous motors]. St. Petersburg, St. Petersburg State University ITMO, 2006. 94 p. (in Russian)
  11. Vinogradov A.B., Sibircev A., Kolodin I. Adaptive-vector control system for a sensorless asynchronous electric drive of the EPV series. Silovaya elektronika [Power electronics], 2006, no. 3, pp. 50-55. (in Russian)
  12. Pankratov V.V., Kotin D.A. Adaptivnye algoritmy bezdatchikovogo vektornogo upravlenija asinhronnymi elektroprivodami podyemno-transportnyh mehanizmov [Adaptive algorithms for sensorless vector control of asyn-chronous electric drives of lifting and transport mechanisms]. Novosibirsk, NGTU, 2012. 143 p. (in Russian)
  13. Moskalenko V.V. Elektricheskij privod [Electric drive]. Moscow, Publishing Center "Academy", 2004. 368 p. (in Russian)
  14. Langraf S.V., Glazyrin A.S., Glazyrina T.A., Afanas'ev K.S., Timoshkin V.V. Investigation of the parametric robustness of a sensorless vector asynchronous electric drive with Kalman identifier. Izvestiya TPU. [Bulletin of TPU], 2010, vol. 317, no. 4, pp. 120-123. (in Russian) 
  15. Glazyrin A.S. Sensorless control of an asynchronous electric drive with a synergistic controller. Izvestiya Tomskogo politekhnicheskogo universiteta [Bulletin of Tomsk Polytechnic University], 2012, vol. 321, no. 4, pp. 107-111. (in Russian)
  16. Afanasev K.S. Razrabotka nabljudatelja sostojanija dlja asinhronnogo elektroprivoda s povyshennoj parametricheskoj robastnostju. Kand.Diss. [Development of a state observer for an asynchronous electric drive with increased parametric robustness. Kand.Diss.]. Tomsk, 2015. 106 p. (in Russian)
  17. Vejnmejster A.V. Kosvennoe izmerenie skorosti vrashhenija v elektroprivode s asinhronnym dvigatele na osnove identifikatora sostojanija. Kand.Diss. [Indirect measurement of the rotation speed in an electric drive with an asynchronous motor based on the state identifier. Abstract of Kand.Diss.]. St. Petersburg, 2013. 17 p. (in Russian)
  18. Meshheryakova O.V., Shishlin D.I., Meshheryakov V.N. Application of neural networks in industrial units. Materialy XII mezhdunarodnoy nauchno-prakticheskoy internet-konferentsii: Energo- i resursoaberezhenie - XXI vek [XII In-ternational Scientific and Practical Internet Conference "En-ergy and Resource Saving - XXI century"]. Orel, OSU Publ., 2016, pp. 243-245. (in Russian)
  19. Raja M.A.Z., Abbas S., Syam M.I., Wazwaz A.W. Design of Neuro-Evolutionary Model for Solving Nonlinear Singularly Perturbed Boundary Value Problemsю Applied Soft Compu-ting, 2018, vol. 62, pp. 373-394.
  20. Fiser J., Sitek P., Vyhlidal T. Neutral PID Control Loop Investigated in Terms of Similarity Theory. IFAC-Papers online. 2018. Vol. 51. No. 14.Pp. 212-217. doi: 10.1016/j.ifacol.2018.07.225
  21. Vila J.-P., Wagner V. Predictive Neuro-Control of Uncertain Systems: Design and Use of a Neuro-Optimizer. Automatice. 2003, vol. 39, no. 5, pp. 767-777. doi: 0.1016/S0005-1098(03)00005-0
  22. Sinyukova T.V., Sentsov E.V., Sinyukov A.V. Neural Net-work Speed Observers. 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA). Lipetsk, Russia, 2019, pp. 320-324. doi: 10.1109/SUMMA48161.2019.8947484
  23. Shihabudheen K.V., Pillai G.N. Recent Advances in Neuro-Fuzzy System: A Survey. Knowledge-Based Systems, 2018, vol. 152, pp. 136-162. doi: 10.1016/j.knosys.2018.04.014
  24. Dotsenko V.V., Sentsov E.V., Litvinenko A.M., Mesheryakov V.N., Valtchev S. A Model for Predicting Wind Speed and the Probability of a Wind Gust. 2021 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021. Lipetsk, 2021, pp. 576-579. doi: 10.1109/SUMMA53307.2021. 9632142
  25. Kubota H., Matsuse K., Nakano T. DSP-Based Speed Adap-tive Flux Observer of Induction Motor. IEEE Trans. Ind. Applicat, 1993, vol. 29, no. 2, pp. 344-348. doi: 10.1109/28.216542
  26. Sinjukova T.V., Sentsov E.V. Synthesis of the rotor frequen-cy of the asynchronous rotor, based on the concept NN. Pribory i sistemy. Upravlenie, kontrol, diagnostika. [Instru-ments and systems. Management, control, diagnostics], 2019, no. 9, pp. 13-20. doi: 10.25791/pribor.09.2019.870 (in Russian)
  27. Sentsov E.V. Neural network observers of the speed of an induction motor. Tendentsii razvitiya sovremennoi nauki: sbornik trudov naucno-prakticheskoy konferentsii studentov i aspirantov Lipetskogo gosudarstvennogo tekhnicheskogo universiteta [Scientific and practical conference of students and graduate students of the Lipetsk State Technical Univer-sity "Trends in the development of modern science"]. Lipetsk, Lipetsk State Technical University Publ., 2021, pp. 109-113. (in Russian)
  28. SentsovE.V., Mesheryakov. V.N. Synthesis of a neuroregulator with predictive control of the electric drive of the actuators of technological units of metallurgical produc-tion. 18th International Scientific Technical Confer-ence Al-ternating Current Electric Drives, ACED 2021. Ekaterinburg, 2021, 9462259. doi: 10.1109/ACED50605. 2021.9462259

Sentsov E.V., Meshcheryakov V.N. Neural Network Speed Observer Development to Improve the Dynamic Stability of a Sensorless Vector Control System. Elektrotekhnicheskie sistemy i kompleksy [Electrotechnical Systems and Complexes], 2022, no. 2(55), pp. 18-24. (In Russian). https://doi.org/10.18503/2311-8318-2022-2(55)-18-24

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