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Open Access

An Integrated Observer Framework Based Mechanical Parameters Identification for Adaptive Control of Permanent Magnet Synchronous Motor

School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Wind Power Business Division, CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, China
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK
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Abstract

An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this paper. Firstly, an integrated observer framework is established for mechanical parameters’ estimation, which consists of an extended sliding mode observer (ESMO) and a Luenberger observer. Aiming at minimizing the influence of parameters coupling, the viscous friction and the moment of inertia are obtained by ESMO and the load torque is identified by Luenberger observer separately. After obtaining estimates of the mechanical parameters, the optimal proportional integral (PI) parameters of the speed-loop are determined according to third-order best design method. As a result, the controller can adjust the PI parameters in real time according to the parameter changes to realize the adaptive control of the system. Meanwhile, the disturbance is compensated according to the estimates. Finally, the experiments were carried out on simulation platform, and the experimental results validated the reliability of parameter identification and the efficiency of the adaptive control strategy presented in this paper.

Keywords

adaptive controlparameter identificationpermanent magnet synchronous motorextended sliding mode observerLuenberger observer

References

[1]

S. Fang, H. Liu, H. Wang, H. Yang, and H. Lin, High power density PMSM with lightweight structure and high-performance soft magnetic alloy core, IEEE Trans. Appl. Superconduct., vol. 29, no. 2, pp. 1–5, 2019.
Crossref Google Scholar
[2]

T. Türker, U. Buyukkeles, and A. F. Bakan, A robust predictive current controller for PMSM drives, IEEE Trans. Ind. Electron., vol. 63, no. 6, pp. 3906–3914, 2016.
Crossref Google Scholar
[3]

Y. Zhao and L. Dong, Robust current and speed control of a permanent magnet synchronous motor using SMC and ADRC, (in Chinese), Control Theory and Technology, vol. 17, no. 2, pp. 190–199, 2019.
Crossref Google Scholar
[4]

Y. Shen, L. Yu, and J. Li, Robust electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption, Complex System Modeling and Simulation, vol. 2, no. 1, pp. 18–34, 2022.
Crossref Google Scholar
[5]

K. -H. Kim and M. -J. Youn, A nonlinear speed control for a PM synchronous motor using a simple disturbance estimation technique, IEEE Trans. Ind. Electron., vol. 49, no. 3, pp. 524–535, 2002.
Crossref Google Scholar
[6]

C. Lian, F. Xiao, S. Gao, and J. Liu, Load torque and moment of inertia identification for permanent magnet synchronous motor drives based on sliding mode observer, IEEE Trans. Power Electron., vol. 34, no. 6, pp. 5675–5683, 2019.
Crossref Google Scholar
[7]

M. S. Carmeli, F. Castelli-Dezza, M. Iacchetti, and R. Perini, Effects of mismatched parameters in MRAS sensorless doubly fed induction machine drives, IEEE Trans. Power Electron., vol. 25, no. 11, pp. 2842–2851, 2010.
Crossref Google Scholar
[8]

A. M. El-Garhy and M. E. El-Shimy, BELBIC for MRAS with highly non-linear process, Alex. Eng. J., vol. 54, no. 1, pp. 7–16, 2015.
Crossref Google Scholar
[9]

I. Benlaloui, S. Drid, L. Chrifi-Alaoui, and M. Ouriagli, Implementation of a new MRAS speed sensorless vector control of induction machine, IEEE Trans. Energy Conversion, vol. 30, no. 2, pp. 588–595, 2015.
Crossref Google Scholar
[10]
H. M. Kojabadi and M. Ghribi, MRAS-based adaptive speed estimator in PMSM drives, in Proc. 9th IEEE International Workshop on Advanced Motion Control, Istanbul, Turkey, 2006, pp. 569–572.
[11]

S. J. Underwood and I. Husain, Online parameter estimation and adaptive control of permanent-magnet synchronous machines, IEEE Trans. Ind. Electron., vol. 57, no. 7, pp. 2435–2443, 2010.
Crossref Google Scholar
[12]

Y. Yu, X. Huang, Z. Li, M. Wu, T. Shi, Y. Cao, G. Yang, and F. Niu, Full parameter estimation for permanent magnet synchronous motors, IEEE Trans. Ind. Electron., vol. 69, no. 5, pp. 4376–4386, 2022.
Crossref Google Scholar
[13]

F. Tinazzi, P. G. Carlet, S. Bolognani, and M. Zigliotto, Motor parameter-free predictive current control of synchronous motors by recursive least-square self-commissioning model, IEEE Trans. Ind. Electron., vol. 67, no. 11, pp. 9093–9100, 2020.
Crossref Google Scholar
[14]

A. Brosch, S. Hanke, O. Wallscheid, and J. Böcker, Data-driven recursive least squares estimation for model predictive current control of permanent magnet synchronous motors, IEEE Trans. Power Electron., vol. 36, no. 2, pp. 2179–2190, 2021.
Crossref Google Scholar
[15]

M. Messaoudi, H. Kraiem, M. B. Hamed, L. Sbita, and M. N. Abdelkrim, A robust sensorless direct torque control of induction motor based on MRAS and extended Kalman filter, Leonardo J. Sci., vol. 12, no. 1, pp. 35–56, 2008.
Google Scholar
[16]

T. Boileau, N. Leboeuf, B. Nahid-Mobarakeh, and F. Meibody-Tabar, Online identification of PMSM parameters: Parameter identifiability and estimator comparative study, IEEE Trans. Ind. Applicat., vol. 47, no. 4, pp. 1944–1957, 2011.
Crossref Google Scholar
[17]
X. Jiang, P. Sun, and Z. Q. Zhu, Modeling and simulation of parameter identification for PMSM based on EKF, in Proc. 2010 Int. Cof. Computer, Mechatronics, Control and Electronic Engineering, Changchun, China, 2010, pp. 345–348.
[18]
T. Boileau, B. Nahid-Mobarakeh, and F. Meibody-Tabar, On-line identification of PMSM parameters: Model-reference vs EKF, in Proc. 2008 IEEE Ind. Applicat. Soc. Annu. Meeting, Edmonton, Canada, 2008, pp. 1–8.
Crossref
[19]
A. Avdeev and O. Osipov, PMSM identification using genetic algorithm, in Proc. 2019 26th Int. Workshop on Electric Drives: Improvement in Efficiency of Electric Drives (IWED), Moscow, Russia, 2019, pp. 1–4.
Crossref
[20]

W. Gong, Z. Liao, X. Mi, L. Wang, and Y. Guo, Nonlinear equations solving with intelligent optimization algorithms: A Survey, Complex System Modeling and Simulation, vol. 1, no. 1, pp. 15–32, 2021.
Crossref Google Scholar
[21]

Y. Tang, L. Li, and X. Liu, State-of-the-art development of complex systems and their simulation methods, Complex System Modeling and Simulation, vol. 1, no. 4, pp. 271–290, 2021.
Crossref Google Scholar
[22]
Y. Shen, D. Wu, and Z. Ji, Model reference fuzzy adaptive control of permanent magnet synchronous motor, in Proc. 2006 Chinese Control Conference, Harbin, China, 2006, pp. 1522–1527.
[23]
S. Wang, G. Yang, Z. Qu, S. Shi, and C. Chen, Identification of PMSM based on EKF and elman neural network, in Proc. 2009 IEEE International Conference on Automation and Logistics, Shenyang, China, 2009, pp. 1459–1463.
[24]

W. Xu, Y. Jiang, and C. Mu, Novel composite sliding mode control for PMSM drive system based on disturbance observer, IEEE Trans. Appl. Superconduct., vol. 26, no. 7, p. 0612905, 2016.
Crossref Google Scholar
[25]

S. Ye and X. Yao, An enhanced SMO-based permanent-magnet synchronous machine sensorless drive scheme with current measurement error compensation, IEEE J. Emerging Select. Topics Power Electronics, vol. 9, no. 4, pp. 4407–4419, 2020.
Crossref Google Scholar
[26]

K. Liu, J. Feng, S. Guo, L. Xiao, and Z. -Q. Zhu, Identification of flux linkage map of permanent magnet synchronous machines under uncertain circuit resistance and inverter nonlinearity, IEEE Trans Ind. Informat., vol. 14, no. 2, pp. 556–568, 2018.
Crossref Google Scholar
[27]

W. Lu, B. Tang, K. Ji, K. Lu, D. Wang, and Z. Yu, A new load adaptive identification method based on an improved sliding mode observer for PMSM position servo system, IEEE Trans. Power Electron., vol. 36, no. 3, pp. 3211–3223, 2020.
Crossref Google Scholar
[28]

B. Wang, Y. Shao, Y. Yu, Q. Dong, Z. Yun, and D. Xu, High-order terminal sliding-mode observer for chattering suppression and finite-time convergence in sensorless SPMSM drives, IEEE Trans. Power Electron., vol. 36, no. 10, pp. 11910–11920, 2021.
Crossref Google Scholar
[29]

L. Yi, C. Zhang, and G. Wang, Research of self-tuning PID for PMSM vector control based on improved KMTOA, International Journal of Intelligent Systems and Applications, vol. 9, no. 3, pp. 60–67, 2017.
Crossref Google Scholar
[30]

D. Xu and Y. Gao, A simple and robust speed control scheme of permanent magnet synchronous motor, (in Chinese), Control Theory and Applications, vol. 2, no. 2, pp. 165–168, 2004.
Crossref Google Scholar
[31]

W. Gao and Z. P. Jiang, Learning-based adaptive optimal output regulation of linear and nonlinear systems: An overview, (in Chinese), Control Theory and Technology, no. 1, pp. 1–19, 2022.
Crossref Google Scholar
[32]

A. Accetta, M. Cirrincione, M. Pucci, and G. Vitale, Neural sensorless control of linear induction motors by a full-order Luenberger observer considering the end effects, IEEE Trans. Ind. Applicat., vol. 50, no. 3, pp. 1891–1904, 2013.
Crossref Google Scholar
[33]

L. He, F. Wang, J. Wang, and J. Rodríguez, Zynq implemented luenberger disturbance observer based predictive control scheme for PMSM drives, IEEE Trans. Power Electron., vol. 35, no. 2, pp. 1770–1778, 2019.
Crossref Google Scholar
[34]

J. Yu, Design of series corrector based on the third-order best design, (in Chinese), Industrial Instrumentation and Automation, no. 6, pp. 109–116, 2013.
Google Scholar
Complex System Modeling and Simulation
Volume 2 Issue 4,
December 2022
Pages 354-367
DOI: 10.23919/CSMS.2022.0022
Cite this article:
Liao Z, Liu Z, Chen L, et al. An Integrated Observer Framework Based Mechanical Parameters Identification for Adaptive Control of Permanent Magnet Synchronous Motor. Complex System Modeling and Simulation, 2022, 2(4): 354-367. https://doi.org/10.23919/CSMS.2022.0022

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Received: 27 May 2022
Revised: 08 August 2022
Accepted: 18 October 2022
Published: 30 December 2022
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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