High-Performance Sensorless PMSM Drive With Extended Kalman Filter

Shi, Keke; Kojori, H.A.; Wu, Bin

doi:10.4271/2002-01-3253

Cited by 5 publications

(13 citation statements)

References 4 publications

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“…Many different control algorithms have been studied and reported in the literature for the speed and torque control of the PMSM [7][8][9][10][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. In the conventional PMSM drive systems, speed and torque control is achieved by obtaining the rotor position or speed information through shaft sensors such as optical encoders, Hall-Effect sensors or resolvers.…”

Section: Technical Background For the Control Of Pmsmmentioning

confidence: 99%

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Extended Kalman Filter Application in Permanent Magnet Synchronous Motor Sensorless Control

Qiu¹

2021

Preprint

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This thesis presents the modeling, analysis, design and experimental validation of a robust sensorless control method for permanent magnet synchronous motor (PMSM) based on Extended Kalman Filter (EKF) to accurately estimate speed and rotor position. Currently, there is no robust position/speed sensorless control method available for the permanent magnet synchronous motor (PMSM) in the published literature. Traditionally, commercial off-the-shelf simulation models for PMSM do not incorporate initial rotor position and simplified steady-state based modeling of the associated power electronic circuitry and controls are used. These limitations have prevented the development and application of a robust real-time sensorless control method with good dynamic performance over the full speed range for the PMSM. The main focus of this thesis is to overcome these limitations. In particular, a detailed real-time PMSM model in MATLAB/Simulink simulation environment is developed which is used to validate the EKF sensorless control method by varying the initial position of the rotor. The proposed position/speed sensorless control based on EKF method along with all the power electronic circuitry are modeled in this simulation environment. This user-friendly simulation and rapid-prototyping platform is then effectively used to predict, analyse, fine-tune and validate proper operation of the proposed EKF sensorless control method for all operating conditions. In particular, different control strategies are reviewed and the performance of the proposed EKF sensorless control method is critically assessed and validated for different types of dynamic and static torque loads. The robustness of the proposed EKF sensorless method is demonstrated by validating proper operation of the closed-loop motor control system for different rotor initial positions and insensitivity of the EKF speed/position estimation method to the PMSM parameter variations. Proper operation of the proposed EKF based sensorless control method for a high speed permanent magnet synchronous machine is verified experimentally in the lab at Honeywell.

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Section: Technical Background For the Control Of Pmsmmentioning

confidence: 99%

“…Method 3: The Extended Kalman Filter (EKF) is reported [11][12][13][14][15][16][17][18][19][20][21] and used to perform state observer based control of the PMSM that is a nonlinear controlled drive system. Figure 1-5 shows the block diagram of an FKF estimator.…”

Section: Technical Background For the Control Of Pmsmmentioning

confidence: 99%

Extended Kalman Filter Application in Permanent Magnet Synchronous Motor Sensorless Control

Qiu¹

2021

Preprint

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“…In the measurement and inside the system if the covariances of conventional PMSM drive systems, speed control is these noises are known [5][6][7]. However it is computationally achieved by obtaining the rotor position or speed expensive and require a high sampling frequency so that a information through shaft sensors such as optical encoders, simple discrete-time equivalent model can be used.…”

Section: Introduction Permanent Magnets In the Rotor The Extended Kamentioning

confidence: 99%

“…The PMSM speed and angle of PM rotor will be estimated by Pseudo-reduced Order Flux Observer 60 and will be used for PMSM control. The motor speed will be 50-estimated by (7) and then rotor position angle will be calculated from PM speed by (8) For fast response and high 40 dynamic motor drives the hysteresis current controller provides the best dynamic results. A three phase IGBT -30 based inverter will be used to run the PMSM.…”

Section: Introduction Permanent Magnets In the Rotor The Extended Kamentioning

confidence: 99%

Sensorless PMSM drive with MRAS-based adaptive speed estimator

Kojabadi

Chang

2006

2006 37th IEEE Power Electronics Specialists Conference

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The permanent magnet synchronous motor is A technique that monitors the third harmonic voltage to increasingly playing an important role in advanced motor obtain the rotor angle has been developed in [2]. This drives. An encoder or resolver attached to the shaft of the technique is applicable to the brushless dc machine with motor typically supplies the feedbacks required for motor trapezoidal back emf waveform, but is of little relevance to speed control. Many advanced motor drives can not tolerate the PMSM, which normally has a sinusoidal air-gap flux the use of these feedback devices because of reliability concerns . . . ' in a harsh environment, price, space and weight limitations. In distri to Tethrsin [3] implem a obsed this paper model reference adaptive system-based adaptive observer to determine the rotor angle. This observer, strategy has been used for speed estimation. In compare to however, was found to be sensitive to mechanical parameters previously developed methods in the literature such as: EKF, such as load torque, viscous and damping friction and neural networks and sliding mode control this method inertia-parameters that are often changing dynamically or are consumes less computational time and it is easy to implement in unknown. In [4], the dependency on the mechanical microcontrollers and DSP. In this proposed method the parameters is removed in an observer-based approach, but Popov's criterion is used for adaptive speed estimation. The the need for an electrical model of the machine remains. For validity of the proposed adaptive strategy has been verified by a salient rotor PMSM application, the position-dependent simulation and experiments, inductance variation can be monitored to derive the rotor angle. This method, however, is unusable for the many machines that are constructed with surface-mounted

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“…This method, however, is unusable for the many machines that are constructed with surface-mounted permanent magnets in the rotor. The Extended Kalman Filter (EKF) is an optimal stochastic observer in the leastsquare sense for estimating the states of dynamic nonlinear systems, and provides optimal filtering of the noises in measurement and inside the system if the covariances of these noises are known [5][6][7]. However it is computationally expensive and require a high sampling frequency so that a simple discrete-time equivalent model can be used.…”

Section: Introductionmentioning

confidence: 99%

MRAS-based adaptive speed estimator in PMSM drives

Kojabadi

Ghribi

9th IEEE International Workshop on Advanced Motion Control, 2006.

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The permanent magnet synchronous motor (PMSM) is increasingly playing an important role in advanced motor drives. An encoder or resolver attached to the shaft of the motor typically supplies the feedbacks required for PMSM torque and speed control. Many advanced motor drives can not tolerate the use of these feedback devices because of reliability concerns in a harsh environment, price, space and weight limitations. In this paper MRAS-based adaptive strategy has been used for speed estimation. In compare to previously developed methods in the literature such as: EKF, sliding mode control, H ∞ control, this method consumes less computational time and it is easy to implement in microcontrollers and DSP. In this proposed method the Popov's criterion is used for adaptive speed estimation. The Popov's criterion is easier than Lyapunov criterion since it does not need for any predetermined function as it is necessary for Lyapunov criterion. The validity of the MRAS-based adaptive strategy has been verified by simulation and experiments.

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High-Performance Sensorless PMSM Drive With Extended Kalman Filter

Cited by 5 publications

References 4 publications

Extended Kalman Filter Application in Permanent Magnet Synchronous Motor Sensorless Control

Extended Kalman Filter Application in Permanent Magnet Synchronous Motor Sensorless Control

Sensorless PMSM drive with MRAS-based adaptive speed estimator

MRAS-based adaptive speed estimator in PMSM drives

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