Abstract
A modified second-order sliding mode control (MSOSMC) combined with radial basis function (RBF) network estimator is developed and proposed to achieve accurate speed tracking performance for synchronous reluctance motor (SynRM). The dynamic model of SynRM system has the properties of parameter variations, external disturbance, and nonlinear friction force. The MSOSMC method that utilizes continuous control input is applied to reduce the chattering phenomenon. Also, this method utilizes two sliding surfaces to solve the problem of system uncertainty and reduce motor power consumption. The RBF network is developed in MSOSMC scheme to estimate the lumped uncertainty in an on-line fashion. The proposed MSOSMC method uses the system error and control input as the convergence criteria. The adaptation scheme adjusts the parameter vectors based on the Lyapunov theorem approach, so that the asymptotic stability of the developed motor system can be guaranteed. Experimental results show that the MSOSMC structure achieves the better tracking performances in terms of root-mean-square error compared with the traditional SOSMC method under different speed tracking conditions.
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References
Wang X, Tan Q, Liu X, Ge B (2016) Improved radial force modeling and rotor suspension dynamics simulation studies for double-winding bearingless switched reluctance motor. Electr Power Compon Syst 45(1):111–120
Otkun Ö, Akpınar AS (2018) An experimental study on the effect of thrust force on motor performance in linear permanent magnet synchronous motors. Electr Power Compon Syst 45(18):2017–2024
Deraz SA, Azazi HZ (2017) Current limiting soft starter for three phase induction motor drive system using PWM AC chopper. IET Power Electron 10(11):1298–1306
Teresa OK, Korzonek M, Tarchała G (2017) Stability analysis of selected speed estimators for induction motor drive in regenerating mode—a comparative study. Power Compon Syst 64(10):7721–7730
Hadla H, Cruz S (2017) Predictive stator flux and load angle control of synchronous reluctance motor drives operating in a wide speed range. IEEE Trans Ind Electron 64(9):6950–6959
Arzhang YT, Paolo P, Gianmario P (2017) Sensorless direct flux vector control of synchronous reluctance motors including standstill, MTPA, and flux weakening. IEEE Trans Ind Electron 53(4):2322–2333
Barcaro M, Bianchi N, Magnussen F (2012) Permanent magnet optimization in permanent magnet assisted synchronous reluctance motor for a wide constant-power speed range. IEEE Trans Ind Electron 59(6):2495–2502
Edwards C, Spurgeon SK (1998) Sliding mode control: theory and applications. Taylor & Francis, London
Lin FJ, Chen SY, Shyu KK, Liu YH (2010) Intelligent complementary sliding-mode control for LUSMs-based x-y-z motion control stage. IEEE Trans Ultrason Ferroelectr Freq Control 57(7):1626–1640
Lin FJ, Shieh HJ, Huang PK, Shieh PH (2008) An adaptive recurrent radial basis function network tracking controller for a two-dimensional piezo-positioning stage. IEEE Trans Ultrason Ferroelectr Freq Control 55(1):183–198
Wai RJ, Lee JD (2008) Adaptive fuzzy-neural-network control for maglev transportation system. IEEE Trans Neural Netw 19(1):54–70
Wai RJ, Lee JD (2007) Dynamic control of maglev transportation system via adaptive fuzzy-neural-network. In: 2007 IEEE international joint conference on neural networks, pp 569–574
Liu G, Zhang X (2008) Robust sliding-mode control for induction motor drive with RBF neural network based rotor speed estimation. In: IEEE international conference on electrical machines and systems (ICEMS), 2008, pp 1282–1286
Fang CH, Huang WN, Teng CC (2005) Adaptive type fuzzy neural-network (FNN) backstepping motion control strategy based on sliding-mode scheme for induction motor drives with robust position tracking capability. In: IEEE international conference on industrial technology, 2005, pp 1030–1035
Rao S, Buss M, Utkin V (2009) Simultaneous state and parameter estimation in induction motors using first- and second-order sliding modes. IEEE Trans Ind Electron 56(9):3369–3376
Zhang Z, Zhu J, Tang R, Bai B, Zhang H (2010) Second order sliding mode control of flux and torque for induction motor. In: Power and energy engineering conference (APPEEC), 2010, pp 1–4
Rashed M, Goh KB, Dunnigan MW, MacConnell PFA, Stronach AF, Williams BW (2005) Sensorless second-order sliding-mode speed control of a voltage-fed induction-motor drive using nonlinear state feedback. IEE Proc Electr Power Appl 152(5):1127–1136
Gonzalez T, Moreno JA, Fridman L (2012) Variable gain super-twisting sliding mode control. IEEE Trans Autom Control 57(8):2100–2105
Utkin V (2013) On convergence time and disturbance rejection of super-twisting control. IEEE Trans Autom Control 58(8):2013–2017
Evangelista C, Puleston P, Valenciaga F, Fridman LM (2013) Lyapunov-designed super-twisting sliding mode control for wind energy conversion optimization. IEEE Trans Ind Electron 60(2):538–545
Di SG, Rivera JD, Meza MA (2014) Sensorless high order sliding mode control of induction motors with core loss. IEEE Trans Ind Electron 61(6):2678–2689
Wei S, Zhou Y, Huang Y (2017) Synchronous motor-generator pair to enhance small signal and transient stability of power system with high penetration of renewable energy. IEEE Access 5:11505–11512
Verstraten T, Furnémont R, Mathijssen G, Vanderborght B, Lefeber D (2016) Energy consumption of geared DC motors in dynamic applications: comparing modeling approaches. IEEE Robot Autom Lett 1(1):524–530
Pairo H, Shoulaie A (2017) Operating region and maximum attainable speed of energy-efficient control methods of interior permanent-magnet synchronous motors. IET Power Electron 10(5):555–567
Gan C, Wu J, Hu Y, Yang S, Cao W, Guerrero JM (2017) New integrated multilevel converter for switched reluctance motor drives in plug-in hybrid electric vehicles with flexible energy conversion. IEEE Trans Power Electron 32(5):3754–3766
Liu K, Fu X, Lin M, Tai L (2016) AC copper losses analysis of the ironless brushless DC motor used in a flywheel energy storage system. IEEE Trans Appl Supercond 26(7):0611105
Zhang Y, Li S, Liu X (2018) Adaptive near-optimal control of uncertain systems with application to underactuated surface vessels. IEEE Trans Control Syst Technol 26(4):1204–1218
Zhang Y, Li S, Liu X (2018) Neural network-based model-free adaptive near-optimal tracking control for a class of nonlinear systems. IEEE Trans Neural Netw Learn Syst 29(12):6227–6241
Zhang Y, Li S (2016) Adaptive near-optimal consensus of high-order nonlinear multi-agent systems with heterogeneity. Automatica 26(7):426–432
Li S, Zhou MC, Luo X (2018) Modified dual neural networks for motion control of redundant manipulators with dynamic rejection of harmonic noises. IEEE Trans Neural Netw Learn Syst 29(10):4791–4801
Zhang Y, Li S (2019) Plume front tracking in unknown environments by estimation and control. IEEE Trans Ind Inf 15(2):911–921
Zhang Y, Li S, Jiang X (2018) Near-optimal control without solving HJB equations and its applications. IEEE Trans Ind Electron 65(9):7173–7184
Jin Long, Li Shuai, La HM, Luo X (2017) Manipulability optimization of redundant manipulators using dynamic neural networks. IEEE Trans Ind Electron 64(6):4710–4720
Acknowledgements
The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract Nos. MOST 105-2622-E-224-010-CC3, MOST 107-2221-E-224-040.
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The author has received research grants from Ministry of Science and Technology of the Republic of China, Taiwan. Wei-Lung Mao declares that there is no conflict of interest regarding the publication of this paper.
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Mao, WL., Chu, CT. & Hung, CW. Synchronous Reluctance Motor Speed Tracking Using a Modified Second-Order Sliding Mode Control Method. Neural Process Lett 51, 251–270 (2020). https://doi.org/10.1007/s11063-019-10085-x
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DOI: https://doi.org/10.1007/s11063-019-10085-x