A comparison of stator flux linkage estimators for a direct torque controlled PMSM drive

Vyncke, Thomas; Boel, René; Melkebeek, Jan

doi:10.1109/iecon.2009.5414686

Cited by 25 publications

(19 citation statements)

References 18 publications

(19 reference statements)

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“…To improve the estimation of stator flux linkage, various estimators have been developed including the back-emf integration methods such as low-pass filtering [11][12][13], and stabilizing the integrator with a PI-corrector or current offset methods [14][15][16][17]. However, these approaches are not designed for real-time estimation.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Direct Torque Control of Surface-Mounted Permanent Magnet Synchronous Motors with Nonlinear Kalman Filtering

Park

Wang

2018

Energies

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Abstract:The demand for sensorless control of surface-mounted permanent magnet synchronous motor drives has grown rapidly. Among various sensorless control techniques developed, Matsui's current model-based approach and the extended Kalman filter approach have gained much attention. However, the performance of these control methods can be severely worsened or may even become unstable under strong disturbances or sensing failures. This paper presents a comparative study of the extended Kalman filter, the resilient extended Kalman filter, and the unscented Kalman filter-based sensorless direct torque and flux control approaches for the surface-mounted permanent magnet synchronous motor drives. Computer simulation studies and hardware implementation results have shown the efficiency and superior performance of the resilient extended Kalman filter and the unscented Kalman filter over the traditional extended Kalman filter for sensorless direct torque control applications.

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Section: Introductionmentioning

confidence: 99%

Sensorless Direct Torque Control of Surface-Mounted Permanent Magnet Synchronous Motors with Nonlinear Kalman Filtering

Park

Wang

2018

Energies

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“…In figure 3 the RMS-values of the errors are found. As shown in [15], the estimations with EKFC and EKFF do not yield better results than the open-loop current model with measured position. This means that the only remaining advantage is the sensorless fashion in which the estimation is executed.…”

Section: Influence Of Parameter Variationsmentioning

confidence: 87%

“…Still it is a simple method, relying on only one parameter R s and independent of the rotor position. An overview and comparison of several improved methods based on this principle is given in [15], where also current model based methods are discussed and compared.…”

Section: A Stator Flux Linkage Estimationmentioning

confidence: 99%

“…Clearly the RMS error of the EKF estimators is very small, even for large deviations of R s . In steady-state the RMS errors of the stator flux angle are also very small [15]. While only the steady state errors have been considered here, it has to be noted that wrong parameter values affect the (starting) transients even more, with errors that are even much larger than in steady state.…”

Section: Influence Of Parameter Variationsmentioning

confidence: 99%

“…The EKF is often discussed for the sensorless control of PMSM's, however focused on the sensorless position estimation needed for field oriented control in the rotor flux reference frame. Few publications discuss the EKF for the estimation of the stator flux linkage vector [9], [10], [15]. Especially a thorough discussion addressing the effect of incorrect parameters (resistance, inductance,rotor flux magnitude) lacks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On Extended Kalman Filters with augmented state vectors for the stator flux estimation in SPMSMs

Vyncke

Boel

Melkebeek

2010

2010 Twenty-Fifth Annual IEEE Applied Power Electronics Conference and Exposition (APEC)

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Abstract-The demand for highly dynamic electrical drives, characterized by high quality torque control, in a wide variety of applications has grown tremendously during the past decades. Direct torque control (DTC) for permanent magnet synchronous motors (PMSM) can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled, based on torque and flux errors. As such the estimation of the stator flux linkage is essential. In the literature several possible solutions for the estimation of the stator flux linkage are proposed. In order to overcome problems associated with the integration of the back-emf, the use of state observers has been advocated in the literature. Several types of state observers have been conceived and implemented for PMSMs, especially the Extended Kalman Filter (EKF) has received much attention. In most reported applications however the EKF is only used to estimate the speed and rotor position of the PMSM in order to realize field oriented current control in a rotor reference frame. Far fewer publications mention the use of an EKF to estimate the stator flux linkage vector in order to apply DTC. Still the performance of the EKF in the estimation of the stator flux linkage vector has not yet been thoroughly investigated. In this paper the performance of the EKF for stator flux linkage is studied and simulated. The possibilities to improve the estimation by augmenting the state vector and the consequences of these alterations are explored. Important practical aspects for FPGA implementation are discussed.

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On‐line estimation of switched reluctance motor parameters

Aguado-Rojas

Maya‐Ortiz

Espinosa-Pérez

2018

Adaptive Control & Signal

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This paper addresses the problem of on-line parameter estimation of switched reluctance motors and provides, to the best of the authors' knowledge, the first result that takes into account the dynamics of both electrical and mechanical subsystems. Based on a widely-used standard model of the motor and establishing a reasonable structure for modeling the unknown load torque, a linear parametric model is derived and a 2-stage identification scheme is proposed and implemented with the recursive least-squares algorithm with forgetting factor.It is assumed that stator voltages and currents and rotor position and speed are available through measurements. Numerical simulations and experimental tests are included for a 3-phase 12/8 switched reluctance motor. The results show a good performance of the proposed methodology, and the estimated parameters are validated through their use in a closed-loop control scheme. Key features of the proposed solution are that it does not rely on locked-rotor tests nor does it assume previous knowledge of the magnetization curves of the motor. The estimation scheme is intended to improve the performance and efficiency of currently available control algorithms, and it is potentially useful for the design of selfcommissioning switched reluctance drives, ie, drives that can automatically adjust the controller parameters for a wide range of motors and loads.

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A comparison of stator flux linkage estimators for a direct torque controlled PMSM drive

Cited by 25 publications

References 18 publications

Sensorless Direct Torque Control of Surface-Mounted Permanent Magnet Synchronous Motors with Nonlinear Kalman Filtering

Sensorless Direct Torque Control of Surface-Mounted Permanent Magnet Synchronous Motors with Nonlinear Kalman Filtering

On Extended Kalman Filters with augmented state vectors for the stator flux estimation in SPMSMs

On‐line estimation of switched reluctance motor parameters

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