Speed-sensorless Predictive Current Controlled PMSM Drive With Adaptive Filtering-based MRAS Speed Estimators

Demir, Rıdvan

doi:10.1007/s12555-022-0698-z

Cited by 4 publications

(7 citation statements)

References 39 publications

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“…Consequently, this can result in distortion of the motor current, torque ripple, and the emergence of other undesirable effects, such as significant harmonic loss. The MRAS method employs a step-by-step identification approach to guarantee the convergence of identification under specific conditions [26,27]. The fundamental principle underlying the MRAS method is the determination of an appropriate adaptive rate parameter.…”

Section: B Parameter Identification Of Pmsmmentioning

confidence: 99%

PMSM Sensorless Control Based on Moving Horizon Estimation and Parameter Self-Adaptation

Chen,

Wan

2024

Electronics

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The field of sensorless control of permanent magnet synchronous motor (PMSM) systems has been the subject of extensive research. The accuracy of sensorless controllers depends on the precise estimation of PMSM state quantities, including rotational speed and rotor position. In order to enhance state estimation accuracy, this paper proposes a moving horizon estimator that can be utilized in the sensorless control system of PMSM. Considering the parameter variations observed in PMSM, a nonlinear mathematical model of PMSM is established. A model reference adaptive system (MRAS) is employed to identify parameters such as resistance, inductance, and magnetic chain in real time. This approach can mitigate the impact of parameter fluctuations. Moving horizon estimation (MHE) is an estimation method based on optimization that can directly handle nonlinear system models. In order to eliminate the influence of external interference and improve the robustness of state estimation, a method based on MHE has been designed for PMSM, and a sensorless observer has been established. Considering the traditional MHE with large computation and high memory occupation, the calculation of MHE is optimized by utilizing a Hessian matrix and gradient vector. The speed and position of the PMSM are estimated within constraints during a single-step iteration. The results of the simulation demonstrate that in comparison to the traditional control structure, the estimation error of rotational speed and rotor position can be reduced by utilizing the proposed method. A more accurate estimation can be achieved with good adaptability and computational speed, which can enhance the robustness of the control system of PMSM.

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Section: B Parameter Identification Of Pmsmmentioning

confidence: 99%

PMSM Sensorless Control Based on Moving Horizon Estimation and Parameter Self-Adaptation

Chen,

Wan

2024

Electronics

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“…The MRAS uses two models: an adaptive model and a reference model. In Figure 3, the traditional MRAS approach is displayed [34,35].…”

Section: Proposed and Traditional Observers 41 Traditional Mras For P...mentioning

confidence: 99%

“…The MRAS uses two models: an adaptive model and a reference model. In Figure 3, the traditional MRAS approach is displayed [34,35]. The motor variable used by the MRAS is a state variable that is derived from both the adjustable and reference models.…”

Section: Traditional Mras For Pmsgsmentioning

confidence: 99%

“…From Equations ( 25) and (26), The motor variable used by the MRAS is a state variable that is derived from both the adjustable and reference models. The stator current calculated from the reference model and the stator current determined by the adjustable model are used in an adaptation model and an adaption mechanism [34]. Figure 3 shows the adaptive mechanism block diagram of the conventional MRAS.…”

Section: Traditional Mras For Pmsgsmentioning

confidence: 99%

“…The measured voltages in the abc frame are transformed to the αβ stationary frame according to Equation (33). Then, the measured voltages in the αβ stationary frame are transformed to the rotating synchronous dq frame using the estimated predicted position (θ FSS ) for each defined speed in Equation (34).…”

Section: Proposed Finite Speed-set Mras (Fss-mras) Of Pmsgsmentioning

confidence: 99%

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Finite Speed-Set Model Reference Adaptive System Based on Sensorless Control of Permanent Magnet Synchronous Generators for Wind Turbines

Hassan,

Adel,

Farhan

et al. 2024

Machines

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This paper proposes a novel finite speed-set model reference adaptive system (FSS-MRAS) based on the current predictive control (CPC) of a permanent magnet synchronous generator (PMSG) in wind energy turbine systems (WETSs). The mathematical models of wind energy systems (WESs) coupled with a permanent magnet synchronous generator (PMSG) are presented in addition to the implementation of the CPC of PMSGs. The proposed FSS-MRAS is based on eliminating the tuning burden of the conventional MRAS by using a limited set of speeds of the PMSG rotor that are employed to predict the rotor speed of the generator. Consequently, the optimal speed of the rotor is the one resulting from the optimization of a proposed new cost function. Accordingly, the conventional MRAS controller is eliminated and the main disadvantage represented in the tuning burden of the constant-gain proportional-integral (PI) controller has been overcome. The proposed FSS-MRAS observer is validated using MATLAB/Simulink (R2023b) at different operating conditions. The results of the proposed FSS-MRAS have been compared with those of the conventional MRAS, which proved the high robustness and reliability of the proposed observer.

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A novel family of online censoring based complex-valued least mean kurtosis algorithms

Çolak Güvenç,

Mengüç

2024

Signal Processing

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Speed-sensorless Predictive Current Controlled PMSM Drive With Adaptive Filtering-based MRAS Speed Estimators

Cited by 4 publications

References 39 publications

PMSM Sensorless Control Based on Moving Horizon Estimation and Parameter Self-Adaptation

PMSM Sensorless Control Based on Moving Horizon Estimation and Parameter Self-Adaptation

Finite Speed-Set Model Reference Adaptive System Based on Sensorless Control of Permanent Magnet Synchronous Generators for Wind Turbines

A novel family of online censoring based complex-valued least mean kurtosis algorithms

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