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Relevant bibliographies by topics / Speed sensorless control

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Academic literature on the topic 'Speed sensorless control'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Speed sensorless control"

1

Usama, Muhammad, and Jaehong Kim. "Robust adaptive observer-based finite control set model predictive current control for sensorless speed control of surface permanent magnet synchronous motor." Transactions of the Institute of Measurement and Control 43, no. 6 (January 10, 2021): 1416–29. http://dx.doi.org/10.1177/0142331220979264.

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The objective of the paper is to present the efficient and dynamic sensorless speed control of a surface permanent magnet synchronous motor (SPMSM) drive at a wide speed range. For high-performance speed sensorless control, a finite control set model predictive current control (FCS-MPCC) algorithm based on a model reference adaptive system (MRAS) is proposed. With the FCS-MPCC algorithm, the inner current control loop is eliminated, and the limitations of the cascaded linear controller are overcome. The proposed speed sensorless control algorithm provides an efficient speed control technique for the SPMSM drive owing to its fast dynamic response and simple principle. The adaptive mechanism is adopted to estimate the rotor shaft speed and position used in FCS-MPCC for dynamic sensorless control. FCS-MPCC uses a square cost function to determine the optimal output voltage vector (VV) from the switching states that give the low cost index. A discrete-time model of a system is used to predict future currents across all the feasible VVs produced by the voltage source inverter. The VV that reduced the cost function is adopted and utilized. Simulation results showed the efficacy of the presented scheme and the viability of the proposed sensorless speed control design under various load conditions at a wide speed operation range.

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Pham, Ngoc Thuy. "Sensorless speed control of SPIM using BS_PCH novel control structure and NNSM_SC MRAS speed observer." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 2657–77. http://dx.doi.org/10.3233/jifs-190540.

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This paper propose a novel Port Controlled Hamiltonian_Backstepping (PCH_BS) control structure with online tuned parameters, in combination with the modified Stator Current Model Reference Adaptive Syatem (SC_MRAS) based on speed and flux estimator using Neural Networks(NN) and sliding mode (SM) for sensorless vector control of the six phase induction motor (SPIM). The control design is based on combination PCH and BS techniques to improve its performance and robustness. The combination of BS_PCH controller with speed estimator can compensate for the uncertainties caused by the machine parameter variations, measurement errors, and external load disturbances, enables very good static and dynamic performance of the sensorless drive system (perfect tuning of the speed reference values, fast response of the motor current and torque, high accuracy of speed regulation) in a wide speed range, and robust for the disturbances of the load, the speed variation and low speed. The proposed sensorless speed control scheme is validated through Matlab-Simulink. The simulation results verify the effectiveness of the proposed control and observer.

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Uyulan, Caglar. "A robust-adaptive linearizing control method for sensorless high precision control of induction motor." Measurement and Control 52, no. 5-6 (April 15, 2019): 634–56. http://dx.doi.org/10.1177/0020294019833072.

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Even if there exists remarkable applications of induction machines in variable speed drives and also in speed sensorless control in the low–high speed region, open/closed loop estimators in the literature utilized on induction machine sensorless position control vary regarding to their accuracies, sensitivity, and robustness with respect to the variation of model parameter. The deterioration of dynamic performance depends on the lack of estimation techniques which provide trustable information on the flux or speed/position over a wide speed range. An effective estimator should handle the high number of parameter and model uncertainties inherent to induction machines and also torque ripple, the compensation of which is crucial for a satisfactory decoupling and linearizing control to provide the accuracy and precision requirements of demanding motion control in the field of robotics/unmanned vehicle. In this study, to address all of the above-mentioned problems, robust-adaptive linearizing schemes for the sensorless position control of induction machines based on high-order sliding modes and robust differentiators to improve performance were designed. The control schemes based on direct vector control and direct torque control are capable of torque ripple attenuation taking both space and current harmonics into account. The simulation results comprise both the estimation and sensorless speed control of induction machines over a wide operation range, especially at low and zero speed, all of which are promising and indicate significant superiority over existing solutions in the literature for the high precision, direct-drive, speed/position sensorless control of squirrel-cage induction machines.

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Hirano, Koichi, Hidehiro Hara, Teruo Tsuji, and Ryuichi Oguro. "Sensorless Speed Control of IPM Motor." IEEJ Transactions on Industry Applications 120, no. 5 (2000): 666–72. http://dx.doi.org/10.1541/ieejias.120.666.

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Jezernik, Karel. "Vss Speed Sensorless Control of PMSM." IFAC Proceedings Volumes 41, no. 2 (2008): 1815–20. http://dx.doi.org/10.3182/20080706-5-kr-1001.00310.

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Zhang, Yong, and Xu-Feng Cheng. "Sensorless Control of Permanent Magnet Synchronous Motors and EKF Parameter Tuning Research." Mathematical Problems in Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/3916231.

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This paper concerns the parameter tuning and the estimated results postprocessing of the extended Kalman filter for the sensorless control application of permanent magnet synchronous motors. At first an extended Kalman filter parameter tuning method is proposed based on the theoretical and simulation analysis of extended Kalman filter parameters. Furthermore, a sensorless control system is proposed based on the parameter tuning method and the simulation analysis of extended Kalman filter estimation results in different reference speeds and different load torques. The proposed sensorless control system consists of two parts. The first one is a module to self-regulate extended Kalman filter parameters. The second part can correct the estimated speed and the estimated rotation angle based on the reference speed and the electromagnetic torque. Finally, simulation results are presented to verify the feasibility and validity of the proposed sensorless control system.

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Asfu, Workagegn Tatek. "Stator Current-Based Model Reference Adaptive Control for Sensorless Speed Control of the Induction Motor." Journal of Control Science and Engineering 2020 (October 14, 2020): 1–17. http://dx.doi.org/10.1155/2020/8954704.

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This paper described that the stator current-based model reference adaptive system (MRAS) speed estimator is used for the induction motor (IM) indirect vector speed control without a mechanical speed sensor. Due to high sensitivity of motor parameters variation at low speed including zero, stability analysis of MRAS design is performed to correct any mismatch parameters value in the MRAS performed to estimate the motor speed at these values. As a result, the IM sensorless control can operate over a wide range including zero speed. The performance of the stator current-based MRAS speed estimator was analyzed in terms of speed tracking capability, torque response quickness, low speed behavior, step response of drive with speed reversal, sensitivity to motor parameter uncertainty, and speed tracking ability in the regenerative mode. The system gives a good performance at no-load and loaded conditions with parameter variation. The stator current-based MRAS estimator sensorless speed control technique can make the hardware simple and improve the reliability of the motor without introducing a feedback sensor, and it becomes more important in the modern AC IM. The sensorless vector control operation has been verified by simulation on Matlab and experimentally using Texas Instruments HVMTRPFCKIT with TMS320 F28035 DSP card and 0.18 kw AC IM.

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Pujar, Jagadish H., and S. F. Kodad. "A Novel Fuzzy Adaptive Speed Estimator for Space Vector Modulated DTFC of AC Drives." Advanced Materials Research 403-408 (November 2011): 4850–58. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4850.

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In this paper a novel sensorless speed control scheme of Induction Motor (IM) by means of Direct Torque Fuzzy Control (DTFC), PI-type fuzzy speed regulator and fuzzy based Model Reference Adaptive Systems (MRAS) speed estimator strategies has been proposed, which seems to be a boom in sensorless speed control schemes of AC drives. Normally, the conventional sensorless speed control performance of IM drive deteriorates at low speed. Hence the attention has been focused to improve the performance of the IM drive at low speed range as well, by implementing fuzzy control strategies. So, this research work describes a novel adaptive fuzzy based speed estimation mechanism which replaces the conventional PI controller used in MRAS adaptation mechanism. The proposed scheme is validated through extensive numerical simulations on MATLAB. The simulated results signify that the proposed control scheme provides satisfactory high dynamic performance and robustness during low speed operations of IM drive compare to conventional sensorless speed estimator of DTFC scheme.

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Tavares Câmara, Helder, Rafael Cardoso, Rodrigo Zelir Azzolin, and Hilton Abílio Gründling. "Low-cost Sensorless Induction Motor Speed Control." Eletrônica de Potência 12, no. 3 (November 1, 2007): 233–43. http://dx.doi.org/10.18618/rep.2007.3.233243.

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Hussien, Mohamed G., Wei Xu, Yi Liu, and Said M. Allam. "Rotor Speed Observer with Extended Current Estimator for Sensorless Control of Induction Motor Drive Systems." Energies 12, no. 19 (September 21, 2019): 3613. http://dx.doi.org/10.3390/en12193613.

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The aim of paper is to investigate an efficient sensorless control method with vector-control technique for the induction motor (IM) drive systems. The proposed technique relies on the indirect rotor-field orientation control scheme (IRFOC). All sensorless control techniques are greatly affected by the observation of the speed estimation procedure. So, an efficacy new algorithm for estimating the rotor speed of the adopted machine is proposed. In addition, a simple effective method to estimate the machine rotor currents is suggested. The adopted rotor-speed observer is based on the concept of IRFOC method and the phase-axis relationships of IM. To ensure the capability of the proposed sensorless speed-control system, a simulation model is developed in the MATLAB/Simulink software environment. The robustness of the new control method is analyzed under parameter uncertainty issue. Furthermore, comprehensive experimental results are obtained. The whole obtained results confirm the validity of the proposed observer for sensorless speed control capability. The given results also verify the effectiveness of the suggested sensorless control system-based IRFOC for speed-control drive systems of IM. Moreover, the results assure that the presented rotor-speed observer is effectively robust via any parameter changes.

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Dissertations / Theses on the topic "Speed sensorless control"

1

Sevinc, Ata. "Speed sensorless control of induction motors." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364962.

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Ozcelik, Eray. "Speed Sensorless Vector Control Of Induction Machine." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606063/index.pdf.

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Focus of this work is closed-loop speed control of an induction machine based on direct field-oriented control (DFOC) algorithm, using estimates of speed and flux observers which utilize only stator current and voltage. Theoretical bases of the algorithms are explained in detail and their performances are investigated with simulations and experiments. Field Orientated Control is based on projections which transform a threephase time and speed dependent system into a two co-ordinate time invariant system. These projections lead to a structure similar to that of a DC machine control. Transformations are done in synchronous frame alligned to d-axis of rotor flux. So rotor flux position must be known accurately to make these transformations. Degined flux observer, in which voltage model is assisted by current model via a closed-loop to compensate voltage model&rsquo
s disadvantages, estimates the position of the rotor flux. Obtaining adequate torque control via FOC, speed loop is closed using conventional PI regulators. Speed feedback is necessary to complete control loop. Model Reference Adaptive System is studied as a speed estimator. Reactive power scheme is applied to MRAS algorithm to estimate rotor speed. In this study, the direct (rotor) flux oriented control system with flux and speed estimators is described and tested in real-time with the starter kit named TMS320F2812 eZdsp DSK and the Embedded Target for the TI C2000 DSP tool of Matlab

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Zhang, Zaining. "Sensorless vector control for induction motors." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340849.

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Cilia, Joseph. "Sensorless speed and position control of induction motor drives." Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362888.

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Kumara, I. N. Satya. "Speed sensorless field oriented control for induction motor drive." Thesis, University of Newcastle Upon Tyne, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430695.

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Rind, S. J. "Speed sensorless induction motor drive control for electric vehicles." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3008062/.

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Fast diminishing fossil fuel resources, deterioration in air quality and concerns for environmental protection, continuously promote the interest in the research and development of Alternative Energy Vehicles (AEVs). Traction motor drive is an integral part and common electric propulsion system in all kinds of AEVs. It plays an utmost significant role in the development of electrified transport industry. Application of Induction Motor (IM) drive is not only limited to the domestic and industrial applications but also has an ubiquitous influence in the modern electrified transport sector. IM is characterized by a simple and rugged structure, operational reliability, low maintenance, low cost, ability to operate in a hostile environment and high dynamic performance. However, IM is one of the widely accepted choices by Electric Vehicles (EVs) manufacturer. At present, Variable speed IM drive is almost replacing the traditional DC motor drive in a wide range of applications including EVs where a fast dynamic response is required. It became possible after the technological advancement and development in the field of power switching devices, digital signal processing and recently intelligent control systems have led to great improvements in the dynamic performance of traction drives. Speed Sensorless control strategies offer better system’s reliability and robustness and reduce the drive cost, size and maintenance requirements. Sensorless IM drives have been applied on medium and high speed applications successfully. However, instability at low speed and under different load disturbance conditions are still a critical problem in this research field and has not been robustly achieved. Some application such as traction drives and cranes are required to maintain the desired level of torque down to low speed levels with uncertain load torque disturbance conditions. Speed and torque control is more important particularly in motor-in-wheel traction drive train configuration EVs where vehicle wheel rim is directly connected to the motor shaft to control the speed and torque. The main purpose of this research is to improve the dynamic performance of conventional proportional-integral controller based model reference adaptive system (PI-MRAS) speed observer by using several speed profiles under different load torque disturbance conditions, which is uncertain during the whole vehicle operation apart from the vehicle own load. Since, vehicle has to face different road conditions and aerodynamic effects which continuously change the net load torque effect on the traction drive. This thesis proposes different novel methods based on the fuzzy logic control (FLC) and sliding mode control (SMC) with rotor flux MRAS. Numerous simulations and experimental tests designed with respect to the EV operation are carried out to investigate the speed estimation performance of the proposed schemes and compared with the PI-MRAS speed observer. For simulation and experimental purpose, Matlab-Simulink environment and dSPACE DS-1104 controller board are used respectively. The results presented in this thesis show great performance improvements of the proposed schemes in speed estimation & load disturbance rejection capability and provide a suitable choice of speed sensoless IM drive control for EVs with cost effectiveness.

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Comanescu, Mihai. "Flux and speed estimation techniques for sensorless control of induction motors." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1116338965.

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Thesis (Ph.D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xv, 109 p.; also includes graphics. Includes bibliographical references (p. 106-109). Available online via OhioLINK's ETD Center

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Chretien, Ludovic. "POSITION SENSORLESS CONTROL OF NON-SALIENT PERMANENT MAGNET SYNCHRONOUS MACHINE." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1145286531.

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Bateman, Christopher John. "Sensorless control strategies for low-cost, high-speed electrical drive systems." Thesis, University of Newcastle Upon Tyne, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607165.

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Brushless machine topologies have highly favourable qualities for certain applications: no commutator means these machines can be operated at extremely high speeds and because there are no brushes to wear out they are very reliable. It is necessary to know the position of the rotor to operate the machine - this function is performed by the commutator in brushed machine types. For a brushless machine some form of position sensor is normally used to provide the required position information, however, this has drawbacks. Some position sensors can be quite costly and they are often unreliable when operated in hot, electrically noisy environments; decreasing the reliability of an otherwise extremely dependable machine. Several 'sensorless' control schemes have been developed over the years to remove the need for position sensors. This EngD thesis is split into two parts. The first part focuses on a 1,600W, lOO,OOOrpm switched reluctance machine and drive system, used in several products produced by Dyson Ltd. An existing, high-speed, sensorless, control strategy is applied to the machine and the stability of the scheme is analysed. The main challenge with applying a sensorless scheme to this system is the varying nature of the DC link voltage, present due to the low DC link capacitance, which is necessary to reduce costs. A major contribution of this work is the meeting of this challenge. The second part of the thesis examines a 200W, lOO,OOOrpm,battery powered brushless DC machine and a 1,600W, lOO,OOOrpm, mains voltage powered brushless DC machine. A low-speed and high-speed sensorless control system is implemented on the 200W system and the same high-speed sensorless scheme is applied to the 1,600W system. The main difficulty with these machines is that they are single-phase and many existing sensorless methods cannot be applied to them. As with the switched reluctance machine in part one, the 1,600W brushless DC machine has a varying DC link voltage. The main aims were to produce extremely low-cost, reliable sensorless systems that will replace the existing position sensors used on the drives and operate the machines to speeds in excess of 100,000rpm.

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Yang, Chao. "Speed Sensorless Control for Induction Motor Drives with AI-Based MRAS." Thesis, University of Newcastle Upon Tyne, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525087.

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Books on the topic "Speed sensorless control"

1

Kaushik, Rajashekara, Kawamura Atsuo, and Matsuse Kouki, eds. Sensorless control of AC motor drives: Speed and position sensorless operation. New York: Institute of Electrical and Electronics Engineers, 1996.

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(Editor), Kaushik Rajashekara, Atsuo Kawamura (Editor), and Kouki Matsuse (Editor), eds. Sensorless Control of Ac Motor Drives: Speed and Position Sensorless Operation. Ieee, 1996.

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3

ANN Based Sensorless Speed Control of BLDC Motor. Kanyakumari, India: ASDF International, 2017.

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4

Speed sensorless induction motor drives for electrical actuators: Schemes, trends and tradeoffs. [Washington, DC]: National Aeronautics and Space Administration, 1997.

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Vaez-Zadeh, Sadegh. Rotor Position and Speed Estimation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0006.

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The ultimate importance of rotor position and speed information in permanent magnet synchronous (PMS) machines control, and the industry interest to the rotor and speed sensorless systems as a cost-saving and practical alternative to the motor control with mechanical sensors are emphasized. Major position and speed estimation schemes are then presented in detail. These are the: back electromotive force (EMF)-based method; flux linkage method; hypothesis rotor position method; saliency-based method, including high frequency signal injection and inverter switching harmonics schemes; and finally, the observer-based method, including state observer and extended Kalman filter-based schemes. Each scheme was discussed by presenting the corresponding fundamental principles, followed by the appropriate motor model, estimation procedure, and the implementation. Demanding criteria such as accuracy, robustness, swiftness, and capability of working over the entire range of motor operation are discussed with each method.

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Book chapters on the topic "Speed sensorless control"

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Shah, Dhruv, Gerardo Espinosa-Pérez, Romeo Ortega, and Michaël Hilairet. "Sensorless Speed Control of PMSM." In AC Electric Motors Control, 311–40. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118574263.ch15.

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Bhavani, N. P. G., M. Aruna, K. Sujatha, R. Vani, and N. Priya. "Sensorless Speed Control of Induction Motor Using Modern Predictive Control." In Advances in Intelligent Systems and Computing, 675–83. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6981-8_53.

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Quan, Longhu, Zhanshan Wang, Xiuchong Liu, and Mingguo Zheng. "Sensorless PMSM Speed Control Based on NN Adaptive Observer." In Advances in Neural Networks – ISNN 2014, 100–109. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12436-0_12.

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Yan, Tao, Jun Liu, and Haiyan Zhang. "Sensorless Vector Control of PMSM in Wide Speed Range." In Communications in Computer and Information Science, 363–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45261-5_38.

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Wang, Gaolin, Guoqiang Zhang, and Dianguo Xu. "Low-Frequency Ratio Sensorless Control for High-Speed PMSM Drives." In Position Sensorless Control Techniques for Permanent Magnet Synchronous Machine Drives, 203–32. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0050-3_7.

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Zhang, Yuhang, Wangyu Qin, Dawei Zheng, Chongxia Zhou, and Jianhui Liu. "Overview of Speed Sensorless Control of Permanent Magnet Synchronous Motors." In Geo-informatics in Sustainable Ecosystem and Society, 240–51. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7025-0_25.

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Beddiaf, Yassine, Fatiha Zidani, and Larbi Chrifi-Alaoui. "Sensorless Speed Control of Induction Motor Used Differential Flatness Theory." In Lecture Notes in Electrical Engineering, 3–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6403-1_1.

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Mohana Lakshmi, J., H. N. Suresh, and Varsha K. S. Pai. "Nonlinear Speed Estimator and Fuzzy Control for Sensorless IM Drive." In Proceedings of First International Conference on Smart System, Innovations and Computing, 307–18. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5828-8_30.

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Arulmozhiyal, R., C. Deepa, and Kaliyaperumal Baskaran. "Optimized Neuro PI Based Speed Control of Sensorless Induction Motor." In Swarm, Evolutionary, and Memetic Computing, 310–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27242-4_36.

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Wu, Qiya, Shaobo Yin, Dangwei Duan, Chunmei Xu, and Lijun Diao. "Pure Electric Brake of the Train with Speed Sensorless Vector Control." In Lecture Notes in Electrical Engineering, 489–97. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2862-0_47.

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Conference papers on the topic "Speed sensorless control"

1

Manikandan, R., R. Arulmoziyal, and K. R. Priyadharsini. "Fuzzy PI based speed sensorless speed control of position sensorless BLDC drive." In IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/ic.2013.0294.

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Stulrajter, Marek, Pavol Makyssn, and Pavol Rafajdus. "Sensorless control of high speed BLDC." In 2017 IEEE International Symposium on Sensorless Control for Electrical Drives (SLED). IEEE, 2017. http://dx.doi.org/10.1109/sled.2017.8078454.

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Das, B., A. Chakrabarti, P. R. Kasari, and N. Sharma. "Sensorless speed control of DC machine." In 2013 International Conference on Power, Energy and Control (ICPEC). IEEE, 2013. http://dx.doi.org/10.1109/icpec.2013.6527723.

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Jezernik, Karel, Aljaz Kapun, and Milan Curkovic. "Robust speed sensorless control of PMSM." In 2008 IEEE International Symposium on Industrial Electronics (ISIE 2008). IEEE, 2008. http://dx.doi.org/10.1109/isie.2008.4676907.

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"Speed sensorless control of electrical machines." In 2010 IEEE International Symposium on Industrial Electronics (ISIE 2010). IEEE, 2010. http://dx.doi.org/10.1109/isie.2010.5637848.

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Khalil, H. K., and E. G. Strangas. "Sensorless speed control of induction motors." In Proceedings of the 2004 American Control Conference. IEEE, 2004. http://dx.doi.org/10.23919/acc.2004.1386723.

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Jia, Xiaoyan, Xue Wang, Xiaohua Xie, and Hong Chen. "Research on speed-sensorless induction motor control system based on AMESim-Simulink simulation." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334659.

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Montanari, Marcello, Sergei Peresada, and Andrea Tilli. "Intrinsic Speed-Sensorless Control of Induction Motor." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.376995.

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Mustafa, Raihana, Zulkifilie Ibrahim, and Jurifa Mat Lazi. "Sensorless adaptive speed control for PMSM drives." In 2010 4th International Power Engineering and Optimization Conference (PEOCO). IEEE, 2010. http://dx.doi.org/10.1109/peoco.2010.5559258.

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Urlep, Evgen, and Karel Jezernik. "Speed Sensorless Variable Structure Control of PMSM." In IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics. IEEE, 2006. http://dx.doi.org/10.1109/iecon.2006.347567.

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