Torque Ripple Minimization of PMSM Using Parameter Optimization Based Iterative Learning Control

Xia, Changliang; Deng, Weitao; Shi, Tingna; Yan, Yan

doi:10.5370/jeet.2016.11.2.425

Cited by 19 publications

(7 citation statements)

References 22 publications

(23 reference statements)

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“…with parameter matrices D 2 , D 1 , while the other is the simplified torque ripple model (20) with parameter matrices Z 1 , Z 0 . This part needs to be done through offline calculation, and for one given PMSM, those corresponding parameter matrices are constant.…”

Section: Minimum Torque Ripple Controllermentioning

confidence: 99%

“…In the closed-loop feedback controller, the reference torque and instantaneous torque ripple are used as the inputs of torque controller to obtain the appropriate harmonic currents. The instantaneous torque ripple is measured by torque transducer [18], estimated from speed variation [19][20][21] or calculated based on mathematical torque model [18]. To improve the bandwidth of the feedback control system, some adaptive control methods such as iterative learning control and adaptive decision fusion are applied [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…The instantaneous torque ripple is measured by torque transducer [18], estimated from speed variation [19][20][21] or calculated based on mathematical torque model [18]. To improve the bandwidth of the feedback control system, some adaptive control methods such as iterative learning control and adaptive decision fusion are applied [19][20][21]. While in the open-loop feedforward controller, pre-programmed appropriate stator current excitations are injected to reduce the torque ripple [22,23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Active torque ripple reduction based on an analytical model of torque

Zhong

Jiang

Zhou

et al. 2017

IET Electric Power Applications

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The vibration caused by torque ripple may degrade the performance of drivetrain, thus the reduction of torque ripple are necessary for hybrid electrical vehicle/electrical vehicle application, while an analytical description of torque will be the basis. In this study, two-dimensional Fourier series supplemented by polynomial fitting is introduced to reconstruct the numerical solution of magnetic coenergy (MCE) from finite element analysis. An analytical torque model, which can describe torque ripple precisely at all working points, is derived from the reconstructed MCE. On the basis of the torque model, the expressions of harmonic currents used to reduce torque ripple are obtained. The proposed expression also satisfies the principle of maximum torque per ampere. Then, a feedforward torque controller based on the expression of harmonic currents is introduced. Meanwhile, a feedback torque controller based on a torque observer is developed as a comparison. The results of simulation and experiments show that both two controllers can reduce the torque ripple in steady state, but the feedforward torque controller has obviously better effects when motor speed increases. Furthermore, the feedforward torque controller is also beneficial to decline torque ripple during transient process of torque response.

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Section: Minimum Torque Ripple Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Active torque ripple reduction based on an analytical model of torque

Zhong

Jiang

Zhou

et al. 2017

IET Electric Power Applications

View full text Add to dashboard Cite

show abstract

“…Abbaszadeh et al considered a PMSM with nonsinusoidal flux distribution and used the least square method to minimize torque ripple [18]. Xia et al discussed torque ripple minimization for a PMSM using an iterative learning technique [19]. Lalezar et al applied an improved MPCC to enhance the steady-state and dynamic response of a PMSM [20].…”

Section: Introductionmentioning

confidence: 99%

Assessing Finite Control Set Model Predictive Speed Controlled PMSM Performance for Deployment in Electric Vehicles

Murali

Sultana

Gade

et al. 2021

WEVJ

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Electric vehicles (EVs) have the main advantage of using sustainable forms of energy to operate and can be integrated into electrical power grids for better energy management. An essential part of the EV propulsion system is the type of motor used to propel the EV. Permanent magnet synchronous motors (PMSMs) have found extensive use due to various advantages such as high power density, excellent torque-to-weight ratio and smooth speed profile over the entire torque range. The objective of this paper was to improve the dynamic response in the speed profile for different driving conditions essential in EVs. This was done by using the finite control set model predictive control (FCS-MPC) algorithm for PMSM and by comparing and evaluating the control strategies of a PMSM used in an EV by taking two case studies. The classical control, namely field-oriented control (FOC), of PMSMs is slow to adopt the dynamic changes in the system. The proposed FCS-MPC algorithm for PMSMs provides an improved dynamic response and a good steady-state response for the different driving conditions shown in both cases. In addition, the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) is used to evaluate the FCS-MPC-controlled PMSM to depict its superior performance by matching its speed profile. The results are verified in the hardware in the loop strategy using OPAL-RT. Both the results confirm that the FCS-MPC algorithm, when compared with the conventional FOC, is superior in aspects of steady-state and dynamic responses for various torque and speed profiles.

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“…Permanent Magnet Synchronous Motor (PMSM) is superior for its high power density, high dynamic response, and high efficiency over wide speed range, and is extensively used in vehicle propulsions, wind power systems, and industrial applications [1][2][3]. The rotor position information is indispensable for speed feed-back control and coordinate transformation in high performance field oriented control systems for PMSM, which is usually obtained through a mechanical position sensor.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Control of Permanent Magnet Synchronous Motors with Compensation for Parameter Uncertainty

Yang¹,

Mao²,

Chen³

2017

Journal of Electrical Engineering and Technology

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-Estimation errors of the rotor speed and position in sensorless control systems of Permanent Magnet Synchronous Motors (PMSM) will lead to low efficiency and dynamic-performance degradation. In this paper, a parallel-type extended nonlinear observer incorporating the nominal parameters is constructed in the stator-fixed reference frame, with rotor position, speed, and the load torque simultaneously estimated. The stability of the extended nonlinear observer is analyzed using the indirect Lyapunov's method, and observer gains are selected according to the transfer functions of the speed and position estimators. Taking into account the parameter inaccuracies issue, explicit estimation error equations are derived based on the error dynamics of the closed-loop sensorless control system. An equivalent flux error is defined to represent the back Electromotive Force (EMF) error caused by the inaccurate motor parameters, and a compensation strategy is designed to suppress the estimation errors. The effectiveness of the proposed method has been validated through simulation and experimental results.

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Torque Ripple Minimization of PMSM Using Parameter Optimization Based Iterative Learning Control

Cited by 19 publications

References 22 publications

Active torque ripple reduction based on an analytical model of torque

Active torque ripple reduction based on an analytical model of torque

Assessing Finite Control Set Model Predictive Speed Controlled PMSM Performance for Deployment in Electric Vehicles

Sensorless Control of Permanent Magnet Synchronous Motors with Compensation for Parameter Uncertainty

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