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Extreme learning machine-based field-oriented feedback linearization speed control of permanent magnetic synchronous motors

  • S.I.: COMPUTATIONAL INTELLIGENCE-BASED CONTROL AND ESTIMATION IN MECHATRONIC SYSTEMS
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Abstract

An extreme learning machine (ELM)-based field-oriented feedback linearization speed control (ELMFOFLC) is proposed to enhance the robustness and tracking performance of a permanent magnetic synchronous motor (PMSM) system. First, the field-oriented control (FOC) is adopted to control the electromagnetic torque and the stator magnetic flux of PMSM independently with a detailed discussion on effects especially brought by the model parameter uncertainties on a FOC-based PMSM model. Then, three field-oriented feedback linearization controllers (FOFLCs) are designed to control the electromagnetic torque loop, the stator magnetic flux loop and the outer speed loop, respectively, and cancel nonlinearities in these three loops. Furthermore, a specific ELM is proposed based on the analysis of the characteristics and the uncertainties of PMSM with FOFLC. The stability is proved using the Lyapunov method. Finally, comprehensive simulations and experiments demonstrate that the proposed control is robust to various uncertainties with a superior speed tracking performance.

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References

  1. Tarczewski T, Grzesiak LM (2016) Constrained state feedback speed control of PMSM based on model predictive approach. IEEE Trans Industr Electron 63(6):3867–3875. https://doi.org/10.1109/TIE.2015.2497302

    Article  Google Scholar 

  2. Aghili F (2018) Optimal feedback linearization control of interior PM synchronous motors subject to time-varying operation conditions minimizing power loss. IEEE Trans Industr Electron 65(7):5414–5421. https://doi.org/10.1109/TIE.2017.2784348

    Article  Google Scholar 

  3. Tarczewski T, Grzesiak LM (2018) An application of novel nature-inspired optimization algorithms to auto-tuning state feedback speed controller for PMSM. IEEE Trans Ind Appl 54(3):2913–2925. https://doi.org/10.1109/TIA.2018.2805300

    Article  Google Scholar 

  4. Apte A, Joshi VA, Mehta H, Walambe R (2020) Disturbance-observer-based sensorless control of PMSM using integral state feedback controller. IEEE Trans Power Electron 35(6):6082–6090. https://doi.org/10.1109/TPEL.2019.2949921

    Article  Google Scholar 

  5. Liu X, Yu H, Yu J, Zhao L (2018) Combined speed and current terminal sliding mode control with nonlinear disturbance observer for PMSM drive. IEEE Access 6:29594–29601. https://doi.org/10.1109/ACCESS.2018.2840521

    Article  Google Scholar 

  6. Perruquetti W, Barbot JP (2002) Sliding mode control in engineering. New York

  7. Han J (2009) From PID to active disturbance rejection control. IEEE Trans Industr Electron 56(3):900–906. https://doi.org/10.1109/TIE.2008.2011621

    Article  Google Scholar 

  8. Liu J (2013) Radial Basis Function (RBF) Neural Network Control for Mechanical Systems. doi:https://doi.org/10.1007/978-3-642-34816-7

  9. Rubio-Astorga G, Sánchez-Torres JD, Cañedo J, Loukianov AG (2014) High-order sliding mode block control of single-phase induction motor. IEEE Trans Control Syst Technol 22(5):1828–1836. https://doi.org/10.1109/TCST.2013.2289307

    Article  Google Scholar 

  10. Huo X, Tong X-G, Liu K-Z, Ma K-M (2016) A compound control method for the rejection of spatially periodic and uncertain disturbances of rotary machines and its implementation under uniform time sampling. Control Eng Pract 53:68–78. https://doi.org/10.1016/j.conengprac.2016.05.001

    Article  Google Scholar 

  11. Yang J, Chen W, Li S, Guo L, Yan Y (2017) Disturbance/uncertainty estimation and attenuation techniques in PMSM drives—a survey. IEEE Transactions Ind Electron 64(4):3273–3285. https://doi.org/10.1109/TIE.2016.2583412

    Article  Google Scholar 

  12. Yan Y, Yang J, Sun Z, Zhang C, Li S, Yu H (2018) Robust speed regulation for PMSM servo system with multiple sources of disturbances via an augmented disturbance observer. IEEE/ASME Trans Mechatron 23(2):769–780. https://doi.org/10.1109/TMECH.2018.2799326

    Article  Google Scholar 

  13. Anastassiou GA (2011) Multivariate hyperbolic tangent neural network approximation. Comput Math Appl 61(4):809–821. https://doi.org/10.1016/j.camwa.2010.12.029

    Article  MathSciNet  MATH  Google Scholar 

  14. Åkesson BM, Toivonen HT, Waller JB, Nyström RH (2005) Neural network approximation of a nonlinear model predictive controller applied to a pH neutralization process. Comput Chem Eng 29(2):323–335. https://doi.org/10.1016/j.compchemeng.2004.09.023

    Article  Google Scholar 

  15. Anastassiou GA (2011) Multivariate sigmoidal neural network approximation. Neural Netw 24(4):378–386. https://doi.org/10.1016/j.neunet.2011.01.003

    Article  MATH  Google Scholar 

  16. Dua V (2011) An artificial neural network approximation based decomposition approach for parameter estimation of system of ordinary differential equations. Comput Chem Eng 35(3):545–553. https://doi.org/10.1016/j.compchemeng.2010.06.005

    Article  Google Scholar 

  17. Anastassiou GA (2012) Fractional neural network approximation. Comput Math Appl 64(6):1655–1676. https://doi.org/10.1016/j.camwa.2012.01.019

    Article  MathSciNet  MATH  Google Scholar 

  18. Hu Y, Wang H, Cao Z, Zheng J, Ping Z, Chen L, Jin X (2019) Extreme-learning-machine-based FNTSM control strategy for electronic throttle. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04446-9

    Article  Google Scholar 

  19. Wang H, Xu Z, Do M, Zheng J, Cao Z, Xie L (2015) Neural-network-based robust control for steer-by-wire systems with uncertain dynamics. Neural Comput Appl 26(7):1575–1586. https://doi.org/10.1007/s00521-014-1819-2

    Article  Google Scholar 

  20. Chuei R, Cao Z, Man Z (2018) Neural Network Super-twisting based Repetitive Control for a Brushless DC Servo Motor with Parameter Uncertainty. Friction, and Backlash. https://doi.org/10.1109/ANZCC.2018.8606543

    Article  Google Scholar 

  21. Huang G-B, Zhu Q-Y, Siew C (2006) Extreme Learning Machine: Theory and Applications. Neurocomputing 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  22. Rong H-J, Zhao G-S (2013) Direct adaptive neural control of nonlinear systems with extreme learning machine. Neural Comput Appl 22(3):577–586. https://doi.org/10.1007/s00521-011-0805-1

    Article  Google Scholar 

  23. Wang N, Sun J, Er MJ, Liu Y (2016) A novel extreme learning control framework of unmanned surface vehicles. IEEE Transactions on Cybernetics 46(5):1106–1117. https://doi.org/10.1109/T-CYB.2015.2423635

    Article  Google Scholar 

  24. Yang C, Huang K, Cheng H, Li Y, Su C (2017) Haptic identification by ELM-controlled uncertain manipulator. IEEE Trans Syst Man Cybern Syst 47(8):2398–2409. https://doi.org/10.1109/TSMC.2017.2676022

    Article  Google Scholar 

  25. Chuei R, Cao Z (2020) Extreme learning machine-based super-twisting repetitive control for aperiodic disturbance, parameter uncertainty, friction, and backlash compensations of a brushless DC servo motor. Neural Comput Appl 32(18):14483–14495. https://doi.org/10.1007/s00521-020-04965-w

    Article  Google Scholar 

  26. Verrelli CM, Bifaretti S, Carfagna E, Lidozzi A, Solero L, Crescimbini F, Benedetto MD (2019) Speed sensor fault tolerant PMSM machines: from position-sensorless to sensorless control. IEEE Trans Ind Appl 55(4):3946–3954. https://doi.org/10.1109/TIA.2019.2908337

    Article  Google Scholar 

  27. Bose B (2002) Modern power electronics and AC drives. Prentice Hall, Upper Saddle River, N.J.

    Google Scholar 

  28. Ortega R, Monshizadeh N, Monshizadeh P, Bazylev D, Pyrkin A (2018) Permanent magnet synchronous motors are globally asymptotically stabilizable with PI current control. Automatica

  29. Na J, Yang J, Wang S, Gao G, Yang C (2019) Unknown Dynamics Estimator-Based Output-Feedback Control for Nonlinear Pure-Feedback Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems PP (99):1–12

  30. Hernandez-Guzman VM, Silva-Ortigoza R (2011) PI control plus electric current loops for PM synchronous motors. IEEE Trans Control Syst Technol 19(4):868–873. https://doi.org/10.1109/TCST.2010.2052103

    Article  Google Scholar 

  31. Mendoza-Mondragón F, Hernández-Guzmán V, CarrilloSerrano RV (2015) Velocity Regulation in Pmsms Using Standard Field Oriented Control Plus Adaptation. Asian Journal of Control

  32. Tu W, Luo G, Chen Z, Cui L, Kennel R (2019) Predictive cascaded speed and current control for PMSM drives with multi-timescale optimization. IEEE Trans Power Electron 34(11):11046–11061. https://doi.org/10.1109/TPEL.2019.2897746

    Article  Google Scholar 

  33. Tu W, Luo G, Chen Z, Liu C, Cui L (2019) FPGA implementation of predictive cascaded speed and current control of PMSM drives with two-time-scale optimization. IEEE Trans Ind Inf 15(9):5276–5288. https://doi.org/10.1109/TII.2019.2897074

    Article  Google Scholar 

  34. Zhiqiang G (2003) Scaling and bandwidth-parameterization based controller tuning. In: Proceedings of the 2003 American Control Conference, 4–6 June 2003 2003. pp 4989–4996. https://doi.org/10.1109/ACC.2003.1242516

  35. Khalil KH (2002) Nonlinear Systems Third Edition. Prentice Hall, Upper Saddle River, N.J.

  36. Jiang Y, Xu W, Mu C, Liu Y (2018) Improved deadbeat predictive current control combined sliding mode strategy for PMSM drive system. IEEE Trans Veh Technol 67(1):251–263. https://doi.org/10.1109/TVT.2017.2752778

    Article  Google Scholar 

  37. Verrelli C, Tomei P (2020) Global stability for the inner and outer PI control actions in non-salient-pole PMSMs. Automatica 117:108988. https://doi.org/10.1016/j.automatica.2020.108988

    Article  MathSciNet  MATH  Google Scholar 

  38. Dai C, Guo T, Yang J, Li S (2021) A disturbance observer-based current-constrained controller for speed regulation of PMSM systems subject to unmatched disturbances. IEEE Trans Ind Electron 68(1):767–775. https://doi.org/10.1109/TIE.2020.3005074

    Article  Google Scholar 

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Correspondence to Song Wang.

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This work was supported in part by the Australian Research Council under Grant DP190101557.

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Appendix

Appendix

The proof of Lemma 1 is given as follows:

According to Eq. (2):

$$\dot{e}_{q} = \dot{I}_{q} - \dot{I}_{q}^{*} = \left( { - \omega_{e} I_{d} - \frac{{R_{s} }}{L}I_{q} - \frac{1}{L}\psi_{r} \omega_{e} + \frac{1}{L}U_{q} + \Delta_{d2} } \right) - \dot{I}_{q}^{*}$$
(38)

Substitute Eq. (23) into Eq. (38):

$$\begin{aligned} \dot{e}_{q} = & - \omega _{e} I_{d} - \frac{{R_{s} }}{L}I_{q} - \frac{1}{L}\psi _{r} \omega _{e} + \frac{1}{L}[L\left( - \omega _{e} I_{d} - \frac{{R_{s} }}{L}I_{q} - \frac{{\psi _{r} }}{L}\omega _{e}\right) \\ & + L\left(\dot{I}_{q}^{*} - K_{{p2}} e_{q} - K_{{i2}} \int e_{q}\right) + \Delta _{{d2}} - \dot{I}_{q}^{*} = - K_{{p2}} e_{q} - K_{{i2}} \int e_{q} + \Delta _{{d2}} \\ \end{aligned}$$
(39)

According to Eq. (2) with \({\mathrm{I}}_{\mathrm{d}}^{*}=0\):

$$\dot{e}_{d} = \dot{I}_{d} - \dot{I}_{d}^{*} = - \frac{{R_{s} }}{L}I_{d} + \omega_{e} I_{q} + \frac{1}{L}U_{d} + \Delta_{d1}$$
(40)

Substitute Eq. (23) into Eq. (40):

$$\dot{e}_{d} = - \frac{{R_{s} }}{L}I_{d} + \omega_{e} I_{q} + \frac{1}{L}\left[ {L\left( {\omega_{e} I_{q} - \frac{{R_{s} }}{L}I_{d} - K_{p3} e_{d} - K_{i3} \int e_{d} } \right)} \right] + \Delta_{d1} = - K_{p3} e_{d} - K_{i3} \int e_{d} + \Delta_{d1}$$
(41)

Choose Lyapunov functions with \({\text{K}}_{\text{p2}}>0\), \({\text{K}}_{\text{p3}}>0\), \({\text{K}}_{\text{i2}}>0\), \({\text{K}}_{\text{i3}}>0\):

$$\left\{ {\begin{array}{*{20}c} {V_{1} = \frac{1}{2}e_{d}^{2} + \frac{{K_{i3} }}{2}(\int e_{d} )^{2} \ge 0} \\ {V_{2} = \frac{1}{2}e_{q}^{2} + \frac{{K_{i2} }}{2}\left(\int e_{q}\right )^{2} \ge 0} \\ \end{array} } \right.$$
(42)

Then, the time derivatives of Eq. (42) are:

$$\left\{ {\begin{array}{*{20}c} {\dot{V}_{1} = e_{d} \dot{e}_{d} + e_{d} K_{i3} \int e_{d} } \\ {\dot{V}_{2} = e_{q} \dot{e}_{q} + e_{q} K_{i2} \int e_{q} } \\ \end{array} } \right.$$
(43)

Substitute Eqs. (39), (41) into (43):

$$\left\{ {\begin{array}{*{20}c} {\dot{V}_{1} = e_{d} \left( { - K_{p3} e_{d} + \Delta_{d1} } \right)} \\ {\dot{V}_{2} = e_{q} \left( { - K_{p2} e_{q} + \Delta_{d2} } \right)} \\ \end{array} } \right.$$
(44)

According to A2, we have \({|\Delta }_{\mathrm{d}1}|\le \mathrm{M}\) and \({|\Delta }_{\mathrm{d}2}|\le \mathrm{M}\),

$$\left\{ {\begin{array}{*{20}c} {\dot{V}_{1} \le - K_{p3} e_{d}^{2} + \left| {e_{d} } \right||\Delta_{d1} | \le - K_{p3} e_{d}^{2} + M\left| {e_{d} } \right|} \\ {\dot{V}_{2} \le - K_{p2} e_{q}^{2} + \left| {e_{q} } \right|\left| {\Delta_{d2} } \right| \le - K_{p2} e_{q}^{2} + M\left| {e_{q} } \right|} \\ \end{array} } \right.$$
(45)

For \({\dot{\mathrm{V}}}_{1}<\) 0 and \({\dot{\mathrm{V}}}_{2}<\) 0, it is sufficient that

$$\left\{ {\begin{array}{*{20}c} {\left| {e_{d} } \right| > \frac{M}{{K_{p3} }}} \\ {\left| {e_{q} } \right| > \frac{M}{{K_{p2} }}} \\ \end{array} } \right.$$
(46)

Therefore, the system will first converge and then confine within

$$\left\{ {\begin{array}{*{20}c} {\left| {e_{d} } \right| \le \frac{M}{{K_{p3} }}} \\ {\left| {e_{q} } \right| \le \frac{M}{{K_{p2} }}} \\ \end{array} } \right.$$
(47)

Here completes the proof.

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Zheng, Y., Cao, Z., Wang, S. et al. Extreme learning machine-based field-oriented feedback linearization speed control of permanent magnetic synchronous motors. Neural Comput & Applic 34, 5267–5282 (2022). https://doi.org/10.1007/s00521-021-06722-z

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