Application of EKF to parameter and state estimation of PMSM drive

Salvatore, L.; Stasi, S.

doi:10.1049/ip-b.1992.0019

Cited by 50 publications

(21 citation statements)

References 1 publication

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“…The relation of the torque and current (3), (8) and (9) can be presented in the d,q coordinates. The MTPA operating point is known as the tangent point of the constant torque and current curves as shown in fig.…”

Section: Mtpa Scheme With Self-correctionmentioning

confidence: 99%

“…The accuracy of the parameter estimation for these mentioned algorithms is determined by the degree of persistent excitation [10], which depends on the operating condition of the IPMSM in case of the parameter estimation. To prevent the ill condition for the estimation, most of the references consider some of the parameters of the IPMSM plant are constant [9], [7], [1], [11]. In order to achieve a full estimation for all parameters of the IPMSM, the RLS combined with signal injection is proposed by [6].…”

Section: Introductionmentioning

confidence: 99%

“…In order to improve the performance of the controller for the IPMSM, several online parameter estimation methods are introduced. The affine projection algorithm (APA), extended Kalman filter (EKF) and recursive least square (RLS) are introduced in [1], [9] and [7] respectively. The accuracy of the parameter estimation for these mentioned algorithms is determined by the degree of persistent excitation [10], which depends on the operating condition of the IPMSM in case of the parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive torque controller with MTPA for an IPMSM using model based self-correction

Liu

Hameyer

2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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In this paper a novel adaptive Maximum Torque per Ampere (MTPA) control scheme of an interior permanent magnet synchronous machine (IPMSM) is introduced. To avoid an on-or off-line numerical calculation of the reference current for the MTPA curve, a reference model of the torque which is equivalent to the IPMSM with zero d-axis current is utilized to enforce the torque of the IPMSM towards its reference value along the MTPA curve dynamically. Considering the parameter variation, an adaptive current controller with parameters and torque estimation using model reference adaptive system (MRAS) is embedded. The uncertainty within the IPMSM plant is estimated by a general voltage error to guarantee the stability of the current control loop. The parameter error of the IPMSM is estimated by a recursive least square method with a forgetting factor (RLS-FF). The stability of the proposed control scheme is proved. The simulation results demonstrate the performance of the adaptive torque controller with MTPA.

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Section: Mtpa Scheme With Self-correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An adaptive torque controller with MTPA for an IPMSM using model based self-correction

Liu

Hameyer

2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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“…Another popular method to improve the robustness of the PMSM drive system is to design a nonlinear adaptive controller. In some references, the self-tuning adaptive controller is implemented with on-line parameter identification using extended Kalman filter (EKF) and recursive least squares (RLS) [9] [10]. This adaptive controller is very robust concerning the parameter errors.…”

Section: Introductionmentioning

confidence: 99%

A robust model reference adaptive controller for the PMSM drive system with torque estimation and compensation

Liu

Thul

Hameyer

2014

2014 International Conference on Electrical Machines (ICEM)

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This paper focuses on a robustness controller and the performance of a permanent magnet synchronous machine (PMSM) with parameter uncertainty. The proposed controller is composed by a model reference adaptive current controller and a torque compensator. An internal model of the PMSM is introduced to define the transient reference behavior of the current and torque. A simple adaptation rule of the model uncertainty based on the Lyapunov theory and Symmetrical Optimum is given. With this approach the performance of the current controller and uncertainty estimation are guaranteed. For the torque estimation, the minimum switching time and the harmonics of the converter as well as the zero order hold in measurement result in steady state estimation error. In this paper, a torque estimator is given to compensate the mentioned torque error. In the end, the simulation results demonstrate the performance of the adaptive controller and the torque estimator.

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“…So there have been extensive researches on estimation of rotor position and speed in PMSM drives. Many algorithms to estimate the rotor position and speed on line, like the direct algorithm using the EMF or flux of the stator [2], MRAC, the algorithm based on observer [3], [4], [5], high frequency injection [6], neural network algorithm [7] etc, are presented at present. The Extend Kalman Filter (EKF) is one of observer algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm Based on EKF to Estimate Rotor Position and Speed for Sensorless PMSM Drivers

Bao-jun

2009

2009 International Conference on Information Engineering and Computer Science

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An algorithm based on extended Kalman Filter applied to the sensorless PMSM drives to estimate rotor position and speed are described in this paper. The EKF is an optimal recursive estimation algorithm for estimating the states of dynamic nonlinear systems. The system simulation model is established in MATLAB/Simulink. And the sensorless PMSM drive system is implemented on the DSP employed Field Oriented Control (FOC) method. The performance of the system is studied by means of simulation and experiment. The tests prove the effectiveness of the study.

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Application of EKF to parameter and state estimation of PMSM drive

Cited by 50 publications

References 1 publication

An adaptive torque controller with MTPA for an IPMSM using model based self-correction

An adaptive torque controller with MTPA for an IPMSM using model based self-correction

A robust model reference adaptive controller for the PMSM drive system with torque estimation and compensation

A Novel Algorithm Based on EKF to Estimate Rotor Position and Speed for Sensorless PMSM Drivers

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