Multiobjective Finite Control Set Model Predictive Control Using Novel Delay Compensation Technique for PMSM

The steady-state error and torque ripple caused by the mismatch or variation of parameters in the deadbeat predictive current control of permanent magnet synchronous motor are presented in this paper. An improved adaptive deadbeat current predictive model only related to inductance parameters is proposed. The influence of inductance variation on the system stability margin is quantitatively analyzed. When the variation is less than 50%, the system performance can be improved obviously. When the variation reaches 50%, the system will oscillate and cannot operate stably. The feed forward control strategy is introduced to improve the stability margin of the system, and the inductance disturbance of the system oscillation is expanded to 67%. Simulations and experiments are carried out for traditional deadbeat current predictive model, adaptive deadbeat predictive model, and adaptive model with feed forward control. Results show that under various working conditions, the effectiveness of the proposed method in eliminating the steadystate error caused by parameter disturbances is verified. The current distortion and torque ripple are also restrained. Both robustness and steady-state performance of the system are improved.INDEX TERMS Permanent magnet synchronous motor (PMSM), stability margin, deadbeat predictive current control (DPCC), parameter mismatch, feed forward control, steady-state performance.

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“…+ ω e (k)ψ f (5) where L represents the actual inductance of SPMSM during operation, ψ f represents the actual flux linkage of permanent magnet.…”

Section: B Conventional Predictive Model Of Dpccmentioning

confidence: 99%

“…The novel delay compensation scheme was composed of two sequential procedures. The torque ripple was decreased by 8% after compensation [5].…”

Section: Introductionmentioning

confidence: 98%

Adaptive Deadbeat Predictive Current Control for PMSM With Feed Forward Method

et al. 2021

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“…Permanent magnet synchronous motor (PMSM) has been widely used in the field of high-performance power transmission and renewable energy generation [1] owing to the advantages of simple structure, reliable operation, high power density and high efficiency. Different control algorithms of PMSM are studied for achieving different control objectives, such as field oriented control (FOC) [2], direct torque control (DTC) [3,4], fuzzy control [5], nonlinear control [6] and model predictive control (MPC) [7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

Lyu

Gao

et al. 2021

Energies

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The control performance of the finite control set model predictive current control (FCS-MPCC) for the interior permanent magnet synchronous machine (IPMSM) depends on the accuracy of the mathematical model. A novel robust model predictive current control method based on error compensation is proposed in order to reduce the parameter sensitivity and improve the current control robustness. In this method, the equivalent parameters are obtained from the known voltage and current information at the past time and the error between the predicted current and the actual current at the present time, which is utilized in the two-step prediction process to compensate the parameter mismatch error. Finally, the optimal voltage vector is selected by the cost function. The proposed method is compared with the traditional model predictive current control method through experiments. The experimental results show the effectiveness of the proposed method.

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“…Due to the previous limitations, practical implementations of FCS-MPCs often use a short prediction horizon. Frequently, a one-sample [15]- [20] prediction is selected. The one-sample prediction is often modified [19], [20] to compensate for the effect of the computational delay.…”

Section: Introductionmentioning

confidence: 99%

A Model Predictive Current Controller With Improved Robustness Against Measurement Noise and Plant Model Variations

Pérez-Estévez

Doval-Gandoy

2021

IEEE Open J. Ind. Applicat.

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This article improves the robustness of a finite control set (FCS)-model predictive controller (MPC) for grid-tied inverters and motor drives applications to plant parameter variations and noise, without reducing its bandwidth or affecting its excellent transient response to disturbances and reference commands. The proposed modification adds an observer to the MPC controller structure, which does not significantly increase the computational burden on the embedded controller. Traditionally, observers are employed to estimate unmeasured variables and cancel the effect of disturbances, but this article employs the observer to estimate a measured variable, the converter output current. This solution leverages the benefits of observers from linear controller theory in order to remove undesired components in the measured current and improve the robustness of the controller; hence it is a valuable solution for practicing power-electronic engineers and researchers in the field of grid-tied inverters and motor drives due to its simplicity compared to some advanced techniques often required in more complex MPC designs. Index Terms-Current controller, L filter, finite control set (FCS) model predictive controller (MPC), voltage source converter (VSC), inverter, observer.

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Multiobjective Finite Control Set Model Predictive Control Using Novel Delay Compensation Technique for PMSM

Cited by 82 publications

References 37 publications

Adaptive Deadbeat Predictive Current Control for PMSM With Feed Forward Method

Adaptive Deadbeat Predictive Current Control for PMSM With Feed Forward Method

An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

A Model Predictive Current Controller With Improved Robustness Against Measurement Noise and Plant Model Variations

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