Robust Predictive Current Control of Permanent-Magnet Synchronous Motors With Newly Designed Cost Function

Liu, Xicai; Zhou, Libing; Wang, Jin; Gao, Xiaonan; Li, Zhixiong; Zhang, Zhiwei

doi:10.1109/tpel.2020.2980930

Cited by 72 publications

(35 citation statements)

References 22 publications

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“…Here, it is noted that the currents are sampled immediately before a new switching state is activated to avoid the current spikes that may occur at the switching instants. Then, with the information from the ( 2) s kT − , ( 16) can be obtained according to (15).…”

Section: ) Estimation Of Current Gradient For All Voltage Vectorsmentioning

confidence: 99%

“…However, although the inductance parameter mismatch can be reduced by employing observers, this introduction can increase the complexities of the control strategies. Instead of using an observer, an alternative solution that utilizes a newly designed cost function in proportional-integral (PI) form is proposed in [15] to improve the robustness of MBPCC. In such a strategy, the past current tracking errors and future tracking errors are included in the cost function to be evaluated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Model-Free Predictive Current Control With Advanced Current Gradient Updating Mechanism

Yao

et al. 2021

IEEE Trans. Ind. Electron.

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Section: ) Estimation Of Current Gradient For All Voltage Vectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Model-Free Predictive Current Control With Advanced Current Gradient Updating Mechanism

Yao

et al. 2021

IEEE Trans. Ind. Electron.

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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%

“…Some scholars have proposed some solutions to this problem. In [14], a new cost function in the form of proportional integral is proposed, which not only reduces the current steady-state error, but also reduces the parameter sensitivity. In [15], a parameter identification algorithm is proposed to get the actual parameter value.…”

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 nonlinear nature of FCS-MPC, it is impossible to use the same mature analysis method as a linear system to evaluate the influence of parameter changes (Bogado et al, 2014). Therefore, in previous studies, the influence of model parameter mismatch has been empirically discussed by studying models under different uncertainties (Kwak et al, 2014;Norambuena et al, 2019;Liu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF

2021

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Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny.

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Robust Predictive Current Control of Permanent-Magnet Synchronous Motors With Newly Designed Cost Function

Cited by 72 publications

References 22 publications

An Improved Model-Free Predictive Current Control With Advanced Current Gradient Updating Mechanism

An Improved Model-Free Predictive Current Control With Advanced Current Gradient Updating Mechanism

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

A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF

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