DC Motor Identification Based on Quasi-Optimal Nonlinear Control Algorithm
DOI:
https://doi.org/10.22213/2413-1172-2021-2-68-76Keywords:
identifiability, DC motor, discrete model, state spaceAbstract
The paper deals with the identification of a direct current (DC) motor based on a quasi-optimal digital control model. The DC motor identification involves specification of motor parameters, such as armature winding resistance, its inductance, stator magnetic flux, viscous friction coefficient in drive supports. These parameters are part of the state matrix and determine the voltage magnitude when implementing a quasi-optimal nonlinear control algorithm. The change of these parameters owing to their degradation or certain conditions of drive operation leads to the discrepancy of the model state to the true one and, as consequence, to the increase in power consumption and time of transients. A methodology for calculating the identification criterion for a nonlinear control system in a discrete form is proposed. The determinant of the measurement matrix is calculated at each step of the discrete time. Their analysis shows that motor identification is possible in transient modes. When the armature winding resistance of the motor deviates from nominal, the transient time and the amount of overshoot increase significantly. When the armature resistance decreases by 25% less than the nominal value, the determinant of the motor measurement matrix reaches the threshold value of the identifiability criterion. Thus, the loss of identifiability indicates the presence of a defect. The obtained results of the study can be used to detect faults in drives.References
Umenoand T., Hori Y. Robust speed control of DC servo-motors using modern two degrees-of-freedom controller design. IEEE Transactionson industrial electronics, Oct. 1991, vol. 38, no. 5, pp. 363-368. DOI: 10.1109/41.97556.
Chevrel P., Sicot L., Siala S. Switched LQ controllers for DC motor speed and current control: a comparison with cascade control. In PESC Record. 27th Annual IEEE Power Electronics Specialists Conference, 1996, vol. 1, pp. 906-912. DOI: 10.1109/PESC.1996.548689.
Rubaai A., Kotaru R. Online identification and control of a DC motor using learning adaptation of neural networks. IEEE Transactions on Industry Applications, May-June 2000, vol. 36, no. 3, pp. 935-942. DOI: 10.1109/28.845075.
Yu G.-R., Hwang R.-C. Optimal PID speed control of brushless DC motors using LQR approach. In Proc. IEEE International Conference on Systems, Man and Cybernetics, October 2004, vol. 1, pp. 473-478. DOI: 10.1109/ICSMC.2004.1398343.
Ruderman M., Krettek J., Hoffmann F., Bertram T. Optimal State Space Control of DC Motor. In IFAC Proceedings Volumes, 2008, vol. 41, Issue 2, pp. 5796-5801. DOI: 10.3182/20080706-5-KR-1001.00977.
Peaucelle D., Ebihara Y. LMI results for robust control design of observer-based controllers, the discrete-time case with polytopic uncertainties. In IFAC Proceedings Volumes, 2014, vol. 47, Issue 3, pp. 6527-6532. DOI: 10.3182/20140824-6-ZA-1003.00218.
Luo H. Plug-and-Play Monitoring and Performance Optimization for Industrial Automation Processes. Springer Fachmedien Wiesbaden GmbH. 2017, 158 p. DOI: 10.1007/978-3-658-15928-3.
Luo H. A data-driven realization of the control-performance oriented process monitoring system, IEEE Trans. on Industrial Electronics, vol. 67, pp. 521-530, 2020. DOI: 10.1109/TIE.2019.2892705.
Бычков М. Г., Кузнецова В. Н. Оптимальное и квазиоптимальное управление позиционным электроприводом по критерию минимума электрических потерь. Теория и практика автоматизированного электропривода // ЭСиК. 2015. № 2 (27). С. 4-11.
Mohlalakoma T. Ngwako, Otis T. Nyandoro, John van Coller, Milka C. Madahana. A Condition for Singular Optimal Control Formulation for a Tandem Pair of Gravity Fed Linear DC Machine Powered Mine Locomotives, IFAC-Papers On Line, 2019, vol. 52, Is. 14, pp 201-206. DOI: 10.1016/j.ifacol.2019.09.188.
Мин Т. А. Оптимальное управление электроприводами с двигателями последовательного возбуждения // Молодые ученые - Хабаровскому краю : материалы XXI Краевого конкурса молодых ученых и аспирантов (Хабаровск, 15-18 января 2019 г.). Хабаровск : Тихоокеанский государственный университет, 2019. С. 149-154.
Рыбушкин Н. А., Афанасьев А. Ю. Оптимальное управление электроприводом с двигателем постоянного тока // Молодежь и наука: актуальные проблемы фундаментальных и прикладных исследований : материалы II Всероссийской национальной научной конференции студентов, аспирантов и молодых ученых (Комсомольск-на-Амуре, 8-12 апреля 2019 г.). Комсомольск-на-Амуре : Комсомольский-на-Амуре государственный университет, 2019. С. 437-439.
Самосейко В. Ф., Ширяев Э. В., Улисский Н. А. Энергетически оптимальное управление электромагнитным моментом реактивного двигателя // Вестник государственного университета морского и речного флота им. адмирала С. О. Макарова. 2021. Т. 13, № 1. С. 126-138. DOI: 10.21821/2309-5180-2021-13-1-126-138.
Ding S.X. Advanced Methods for Fault Diagnosis and Fault-tolerant Control. Springer-Verlag GmbH Germany, 2021, 658 p. DOI: 10.1007/978-3-662-62004-5.
Kabziński J. (ed.) Advanced Control of Electrical Drives and Power Electronic Converters. Studies in Systems, Decision and Control, vol. 75. Springer International Publ., Switzerland, 2017, 378 p. DOI: 10.1007/ 978-3-319-45735-2.
Hughes A., Drury B. Electric Motors and Drives: Fundamentals, Types and Applications. Fifth Edition, 2019, Elsevier Ltd, 495 p.
Tang M. Cost-sensitive large margin distribution machine for fault detection of wind turbines, Cluster Computing, 2018.
Wang D.-Y. Connotation and research status of diagnosability of control systems: A review, Acta Automatica Sinica, 2018, vol. 44, pp. 1537-1553.
Xue T. Stationary wavelet transform aided design of parity space vectors for fault detection in LDTV systems, IET Control Theory and Applications, 2018, vol. 12, pp. 857-864.
Yin S. Review on diagnosis techniques for intermittent faults in dynamic systems, IEEE Trans. on Indus. Electronics, 2020, vol. 67, pp. 2337-2347.
Nemeth M., Peterkova A. Proposal of data acquisition method for industrial processes in automotive industry for data analysis according to Industry 4.0. Book Series: IEEE Intern. Conf. on Intellig. Engineering Systems, 2018, pp. 157-161.
Nemeth M. Determination issues of data mining process of failures in the production systems. Book Series: Advance in Intelligent Systems and Computing 2019, 985, pp. 200-207.
