Sensorless vector and speed control of brushless motor drives

Consoli, A.; Musumeci, S.; Raciti, A.; Testa, A.

doi:10.1109/41.281613

Cited by 78 publications

(15 citation statements)

References 4 publications

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“…The stator flux is selected as a state variable in the adjustable model. The model is based on (1) and (2), and defined bẏ…”

Section: Adaptive Observermentioning

confidence: 99%

“…The stator flux and its position can be directly estimated from the motor terminal voltages and currents [1], and the load angle between the rotor and the stator flux can be compensated assuming steady state [2]. By using an adaptive (closed-loop) observer, the noise in the estimated angle can be reduced without degrading the dynamic performance.…”

Section: Introductionmentioning

confidence: 99%

“…The structure of the observer becomes more complicated if an extended EMF [10] is selected as a state variable. It is typical of interior magnet motors that the rotor saliency complicates the application and analysis of fundamental-excitation methods [2], [5], [7]- [10], contrary to signal injection methods. The stability of the drive system is the most crucial design criterion [6], [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of an Adaptive Observer for Sensorless Control of Interior Permanent Magnet Synchronous Motors

Piippo¹,

Hinkkanen²,

Luomi³

2008

IEEE Trans. Ind. Electron.

179

110

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Abstract-This paper deals with a speed and position estimation method for the sensorless control of permanent magnet synchronous motors. The method is based on a speed-adaptive observer. The dynamics of the system are analyzed by linearizing both the motor model and the observer, and the observer gain is selected to give improved damping and noise suppression. At low speeds, the observer is augmented with a signal injection technique, providing stable operation down to zero speed. The experimental results, obtained using a 2.2-kW interior magnet motor, are in agreement with the results of the analysis.

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“…The stator flux is selected as a state variable in the adjustable model. The model is based on (1) and (2), and defined bẏ…”

Section: Adaptive Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of an Adaptive Observer for Sensorless Control of Interior Permanent Magnet Synchronous Motors

Piippo¹,

Hinkkanen²,

Luomi³

2008

IEEE Trans. Ind. Electron.

179

110

View full text Add to dashboard Cite

show abstract

“…For example, the rotor speed and position can be estimated based on the stator voltage equation of the AC motor [3], reference model of the AC motor [4], state observer [5], back EMF [6], the Kalman filtering [7], nonlinear control [8], signal injection [9] and fuzzy control [10].…”

Section: Introductionmentioning

confidence: 99%

Sensorless Vector Control of Induction Motors for Wind Energy Applications Using MRAS and ASO

Jeong¹,

Choi²,

Park³

2014

Journal of Electrical Engineering and Technology

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-Speed sensorless modes of operation are becoming standard solution in the area of electric drives. This paper presents flux estimator and speed estimator for the speed sensorless vector control of induction motors. The proposed sensorless methods are based on the model reference adaptive system (MRAS) observer and adaptive speed observer (ASO). The proposed speed estimation algorithm can be employed in the power control of grid connected induction generator for wind power applications. Two proposed schemes are verified through computer simulation PSIM and compared their simulation results.

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“…Considered about the advantages of brushless motor caused by its structure and material of magnet [9] compared with traditional DC motor, Cho replaced the DC motor with 3-phase brushless direct current (BLDC) motor. For testing the maximum torque that the system can produce practically, a Hysteresis Brake (HB) is added to the plant.…”

Section: Previous Work By Kwanghyeon Chomentioning

confidence: 99%

robust-adaptive control strategy for gear bearing drive systems

Jiang¹

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In modern industrial engineering such as space robots, assembly systems and transportation vehicles, a Gear Bearing Drive (GBD) transmission, with the potential to produce up to 5000:1 torque ratio in a compact size, can be of great interest.However, in a practical actuation system consisting of actuator, GBD and workload, ever-present nonlinearities such as disturbances and perturbations can increase the complexity of the modeling process as well as difficulty of the control design.To reduce such complexities, a simplified model of the actuation plant is developed by linearizing all nonlinearities and numerically estimating the disturbances and perturbations inside the plant. For this, a robust-adaptive control platform, especially designed to deal with system uncertainties and unmodeled dynamics, is selected, analyzed and implemented in real-time. In addition, due to the initially unknown parameters, the adaptive part of the controller must adapt to the system parameters variations and changes. Finally, to guarantee asymptotic convergence, a projection operator is developed and combined with the adaptation law. Furthermore, an optimal combination of proportional-integral-derivative (PID) and robust-adaptive sliding mode control (RASMC) is selected and a novel controller is developed and experimentally implemented and verified.The experimental results of system response are compared to the simulation results to assess control performance and response characteristics. This research suggests that RASMC can ensure its effectiveness in a nonlinear plant with uncertain parameters and unmodeled dynamics and produce a satisfactory experimental performance with minimum settling time and overshoot. Future work is required to explain unpredictable piecewise behavior of the open-loop speed response and other ignored modeling aspects and nonlinearities.iii is taken into consideration.To solve the effect of unknown perturbations, SMC combined with perturbation estimation (SMCPE), which handles with the structured parameter without upper bound knowledge of the disturbance [6,7], is developed to enhance the tracking performance. Previous Work on GBD Previous Work by Elias BrassitosElias Brassitos was a Ph.D. student working in the Piezoactive System Laboratory until 2016. He invented GBD transmission in the earlier 2016. By using the prototype of Epicyclic Gearing System (EGS) combined with the level principle in the designing process, GBD transmission could produce high torque ratio in the practical operation within a compact size. The efficiency improves with higher torques output and reaches a maximum of 83% [2, pp. 102].In the practical system, because of the pervasive nonlinearities existing in the system such as gear tooth friction, stiffness, kinematic error and nonlinear damping, precise model of the actuation plant is difficult to generate. In the modeling process made by Brassitos, kinematic error and stiffness have been considered and the simplified model of the GBD transmission was given out. Previous W...

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Sensorless vector and speed control of brushless motor drives

Cited by 78 publications

References 4 publications

Analysis of an Adaptive Observer for Sensorless Control of Interior Permanent Magnet Synchronous Motors

Analysis of an Adaptive Observer for Sensorless Control of Interior Permanent Magnet Synchronous Motors

Sensorless Vector Control of Induction Motors for Wind Energy Applications Using MRAS and ASO

robust-adaptive control strategy for gear bearing drive systems

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