Full Text:   <2021>

Summary:  <126>

CLC number: TK83; TN06

On-line Access: 2022-02-28

Received: 2020-07-03

Revision Accepted: 2022-04-22

Crosschecked: 2021-06-13

Cited: 0

Clicked: 4100

Citations:  Bibtex RefMan EndNote GB/T7714


Lingfei XIAO


Leiming MA


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.2 P.328-338


Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer

Author(s):  Lingfei XIAO, Leiming MA, Xinhao HUANG

Affiliation(s):  College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; more

Corresponding email(s):   lfxiao@nuaa.edu.cn

Key Words:  Permanent magnet synchronous motor, Fractional-order integral sliding mode, Optimization algorithm, Sensorless control, Observer

Lingfei XIAO, Leiming MA, Xinhao HUANG. Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 328-338.

@article{title="Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer",
author="Lingfei XIAO, Leiming MA, Xinhao HUANG",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer
%A Lingfei XIAO
%A Leiming MA
%A Xinhao HUANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 2
%P 328-338
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000317

T1 - Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer
A1 - Lingfei XIAO
A1 - Leiming MA
A1 - Xinhao HUANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 2
SP - 328
EP - 338
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000317

In this paper, an intelligent fractional-order integral sliding mode control (FOISMC) strategy based on an improved cascade observer is proposed. First, an FOISMC strategy is designed to control a permanent magnet synchronous motor. It has good tracking performance, is strongly robust, and can effectively reduce chattering. The proposed FOISMC strategy associates strong points of the integral action (which can eliminate steady-state tracking errors) and the fractional calculus (which is flexible). Second, an improved cascade observer is proposed to detect the rotor information with a smaller observation error. The proposed observer combines an adaptive sliding mode observer and an extended high-gain observer. In addition, an improved variable-speed grey wolf optimization algorithm is designed to enhance controller parameters. The effectiveness of the strategy is tested using simulations and an experiment involving model uncertainty and external disturbance.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Apte A, Thakar U, Joshi V, 2019. Disturbance observer based rotor speed control of PMSM using fractional order PI controller. IEEE/CAA J Autom Sin, 6(1):316-326. doi: 10.1109/JAS.2019.1911354

[2]Azizi A, Nourisola H, Shoja-Majidabad S, 2019. Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller. Renew Energy, 135:55-65. doi: 10.1016/j.renene.2018.11.106

[3]Bakkyaraj T, Sahadevan R, 2015. Invariant analysis of nonlinear fractional ordinary differential equations with Riemann-Liouville fractional derivative. Nonl Dynam, 80(1):447-455. doi: 10.1007/s11071-014-1881-4

[4]Fei JT, Chen Y, 2020. Dynamic terminal sliding-mode control for single-phase active power filter using new feedback recurrent neural network. IEEE Trans Power Electron, 35(9):9904-9922. doi: 10.1109/TPEL.2020.2974470

[5]Fei JT, Feng ZL, 2020. Fractional-order finite-time super-twisting sliding mode control of micro gyroscope based on double-loop fuzzy neural network. IEEE Trans Syst Man Cybern Syst, 51(12):7692-7706. doi: 10.1109/TSMC.2020.2979979

[6]Fei JT, Wang H, 2020. Experimental investigation of recurrent neural network fractional-order sliding mode control of active power filter. IEEE Trans Circ Syst II, 67(11):2522-2526. doi: 10.1109/TCSII.2019.2953223

[7]Foo G, Rahman MF, 2010. Sensorless sliding-mode MTPA control of an IPM synchronous motor drive using a sliding-mode observer and HF signal injection. IEEE Trans Ind Electron, 57(4):1270-1278. doi: 10.1109/TIE.2009.2030820

[8]Hamida MA, de Leon J, Glumineau A, 2017. Experimental sensorless control for IPMSM by using integral backstepping strategy and adaptive high gain observer. Contr Eng Pract, 59:64-76. doi: 10.1016/j.conengprac.2016.11.012

[9]Kivanc OC, Ozturk SB, 2018. Sensorless PMSM drive based on stator feedforward voltage estimation improved with MRAS multiparameter estimation. IEEE/ASME Trans Mechatron, 23(3):1326-1337. doi: 10.1109/TMECH.2018.2817246

[10]Ma L, Xiao L, Yang J, et al., 2021. Sensorless intelligent second-order integral sliding mode maximum power point tracking control for wind turbine system based on wind speed estimation. Proc Inst Mech Eng, 235(7):1046-1063. doi: 10.1177/0959651820982405

[11]MacDonald CL, Bhattacharya N, Sprouse BP, et al., 2015. Efficient computation of the Grünwald-Letnikov fractional diffusion derivative using adaptive time step memory. J Comput Phys, 297:221-236. doi: 10.1016/j.jcp.2015.04.048

[12]Mirjalili S, Mirjalili SM, Lewis A, 2014. Grey wolf optimizer. Adv Eng Softw, 69:46-61. doi: 10.1016/j.advengsoft.2013.12.007

[13]Nguyen AT, Rafaq MS, Choi HH, et al., 2018. A model reference adaptive control based speed controller for a surface-mounted permanent magnet synchronous motor drive. IEEE Trans Ind Electron, 65(12):9399-9409. doi: 10.1109/TIE.2018.2826480

[14]Pan I, Das S, 2016. Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO. ISA Trans, 62:19-29. doi: 10.1016/j.isatra.2015.03.003

[15]Poli R, Kennedy J, Blackwell T, 2007. Particle swarm optimization. Swarm Intell, 1:33-57. doi: 10.1007/s11721-007-0002-0

[16]Shi TN, Wang Z, Xia CL, 2015. Speed measurement error suppression for PMSM control system using self-adaption Kalman observer. IEEE Trans Ind Electron, 62(5):2753-2763. doi: 10.1016/j.isatra.2015.03.003

[17]Thakar U, Joshi V, Vyawahare V, 2017. Design of fractional-order PI controllers and comparative analysis of these controllers with linearized, nonlinear integer-order and nonlinear fractional-order representations of PMSM. Int J Dynam Contr, 5(1):187-197. doi: 10.1007/s40435-016-0243-0

[18]Tran DC, Wu ZJ, Wang ZL, et al., 2015. A novel hybrid data clustering algorithm based on artificial bee colony algorithm and K-means. Chin J Electron, 24(4):694-701. doi: 10.1049/cje.2015.10.006

[19]Waheed A, Rehman AU, Qureshi MI, et al., 2019. On Caputo k-fractional derivatives and associated inequalities. IEEE Access, 7:32137-32145. doi: 10.1109/ACCESS.2019.2902317

[20]Wang Y, Geng L, Hao WJ, et al., 2018. Control method for optimal dynamic performance of DTC-based PMSM drives. IEEE Trans Energy Conver, 33(3):1285-1296. doi: 10.1109/TEC.2018.2794527

[21]Wu SF, Zhang JW, 2018. A terminal sliding mode observer based robust backstepping sensorless speed control for interior permanent magnet synchronous motor. Int J Contr Autom Syst, 16(6):2743-2753. doi: 10.1007/s12555-017-0806-7

[22]Xie YL, Tang XQ, Song B, et al., 2018. Data-driven adaptive fractional order PI control for PMSM servo system with measurement noise and data dropouts. ISA Trans, 75:172-188. doi: 10.1016/j.isatra.2018.02.018

[23]Yan JD, Wang H, Huang SD, et al., 2019. Load disturbance observer-based complementary sliding mode control for PMSM of the mine traction electric locomotive. Int J Fuzzy Syst, 21(4):1051-1058. doi: 10.1007/s40815-018-0579-z

[24]Yang B, Yu T, Shu HC, et al., 2019. Sliding-mode perturbation observer-based sliding-mode control design for stability enhancement of multi-machine power systems. Trans Inst Meas Contr, 41(5):1418-1434. doi: 10.1177/0142331218783240

[25]Zhang HS, Wang P, Han BC, et al., 2014. Rotor position measuring method for magnetic levitation high speed PMSM based on fuzzy sliding mode observer. Trans China Electrotech Soc, 29(7):147-153 (in Chinese). doi: 10.3969/j.issn.1000-6753.2014.07.020

[26]Zhao Y, Qiao W, Wu L, 2013. An adaptive quasi-sliding-mode rotor position observer-based sensorless control for interior permanent magnet synchronous machines. IEEE Trans Power Electron, 28(12):5618-5629. doi: 10.1109/TPEL.2013.2246871

[27]Zhu AJ, Xu CP, Li Z, et al., 2015. Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC. J Syst Eng Electron, 26(2):317-328. doi: 10.1109/JSEE.2015.00037

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE