PMSM Model Predictive Control With Field-Weakening Implementation

Mynář, Zbyněk; Veselý, Libor; Václavek, Pavel

doi:10.1109/tie.2016.2558165

Cited by 141 publications

(51 citation statements)

References 28 publications

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“…It uses the mathematical model of the control object to control the motion trend of the object through rolling prediction. The performance index function is used to narrow the error between the actual operation and the predicted trajectory to obtain the optimal control amount [8][9][10][11][12][13][14]. Currently, there are many articles about model predictive control of PMSM.…”

Section: Introductionmentioning

confidence: 99%

Single‐loop model prediction control of PMSM with moment of inertia identification

Liu

Erliang

Cui

2020

IEEJ Transactions Elec Engng

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In order to improve the dynamic response performance and robustness of the permanent magnet synchronous motor control system and optimize the parameter adjustment of the control system, a motor control method based on single‐loop model prediction and moment of inertia parameter identification with forgetting factor is proposed. First, the motor state equation in the prediction model is designed, and the single‐loop model predictive control method is proposed. Then, comparing the effects of various parameters of the motor on the running stability of the motor, a least‐squares identification method based on forgetting factor is added to identify the moment of inertia online and optimize the prediction model in real time. The simulation and experimental results show that the proposed method has good dynamic performance for the permanent magnet synchronous motor adapting to the rotational speed and load variation. At the same time, the addition of the moment of inertia identification enhances the accuracy and system robustness of the motor model parameters during the model prediction process. This method provides an effective way of engineering control. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Introductionmentioning

confidence: 99%

Single‐loop model prediction control of PMSM with moment of inertia identification

Liu

Erliang

Cui

2020

IEEJ Transactions Elec Engng

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“…Cascade control with PI still has problems related to impossible performance optimization and difficult fine tuning for improving the dynamic performance or the transient response of the motor. Because PMSM has nonlinear behavior, unmeasured disturbance like load torque, and parameter variations like friction force and rotor inertia, advanced nonlinear control, and disturbance rejection methods were proposed to compensate these disturbances and variations …”

Section: Introductionmentioning

confidence: 99%

“…Because PMSM has nonlinear behavior, unmeasured disturbance like load torque, and parameter variations like friction force and rotor inertia, advanced nonlinear control, and disturbance rejection methods were proposed to compensate these disturbances and variations. [1][2][3] Conventional predictive control (CPC) is basically evaluating the control signals based on minimizing the cost function which is a quadratic performance index of the error between the actual output and the predicted reference output to be tracked. This cost function cannot compensate this error completely for highly nonlinear processes, and in this case, CPC List of abbreviation and symbols: R, the stator resistance; L d , the d-axis stator inductance; L q , the q-axis stator inductance; J, inertia moment; B, viscous friction coefficient; i d , the d-axis stator current; i q , the q-axis stator current; v d , the d-axis stator voltage; v q , the q-axis stator voltage; ω s , the electrical rotor speed; λ af , the rotor permanent magnet flux; T e , the developed torque; T L , the applied load torque; k, the current sampling instant; k + 1, the sampling instant…”

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confidence: 99%

Nonlinear adaptive extended state space predictive control of permanent magnet synchronous motor

Hagras

2018

Int Trans Electr Energ Syst

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Summary This paper proposes new adaptive predictive control approach to control the current loop of the surface permanent magnet synchronous motor (SPMSM) called as extended state space predictive control. The state space model of SPMSM was extended by taking the past inputs as system state variables in the state space model. Then, a new state space model based on the state changes and output tracking error as the new states for new extended state space model was proposed for SPMSM. Accordingly, a new cost function was proposed which provided the change of the control input. Therefore, the simulation results proved that the proposed method had improved reference tracking, fast transient response, compensation of parameter variations, and disturbance attenuation compared to PI controller in field‐oriented vector control scheme.

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“…However, the flux observation is limited by the rotor position in this strategy. A linearized and constrained model predictive control is put forward to the flux-weakening control in [8]. However, this method involves a large amount of calculation and is extremely sensitive to parameter variation.…”

mentioning

confidence: 99%

An Improved UDE-Based Flux-Weakening Control Strategy for IPMSM

Zhang

et al. 2019

Energies

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Interior permanent magnet synchronous motors (IPMSMs) are usually used in electric vehicle drives and in other applications. In order to enlarge the speed range of IPMSMs, the flux-weakening control method is adopted. The traditional flux-weakening control strategy degrades the control performance because of parameter mismatches caused by variation of motor parameters. An improved uncertainty and disturbance estimator (UDE)-based flux-weakening control strategy is proposed for IPMSM drives in this paper. The parameter tuning method in the UDE-based control is improved. In addition, a flux-weakening adjusting factor is put forward to reduce the torque fluctuation when the operation point switches between the constant torque region and the flux-weakening region. This factor can be adjusted online by a lookup table. Finally, the validity of proposed method is verified by the simulation and experimental results. The results show that the proposed control strategy can effectively enhance the robustness of the system in the flux-weakening region, and make the system switch more smoothly between the constant torque region and the flux-weakening region.Energies 2019, 12, 4077 2 of 17 method. The influence of magnetic saturation and stator resistance are considered in [7], and an optimal control strategy is proposed. However, the flux observation is limited by the rotor position in this strategy. A linearized and constrained model predictive control is put forward to the flux-weakening control in [8]. However, this method involves a large amount of calculation and is extremely sensitive to parameter variation. In order to suppress the influence of parameter variation on the system, a voltage feedback control strategy is applied in the flux-weakening region. A flux-weakening current output by a proportional integral (PI) controller is added to the d-axis current reference. The input of the PI controller is the difference between the output voltage amplitude and the maximum available voltage amplitude of inverter. This method is simple, independent of motor parameters, and has good robustness, but the dynamic performance needs to be improved. The conventional voltage feedback method is improved in [9]. The difference between DC link voltage and output voltage of current controller is used to calculate the phase angle of reference current space vector by an adaptive algorithm. However, the global stability of the IPMSM system cannot be guaranteed. A single current regulator is proposed to improve the voltage utilization of the DC bus in [10]. This method eliminates the poor effect caused by the coupling of the d and q axis current, but the decrease of efficiency and stability is still not to be ignored. A line modulation-based flux-weakening control was proposed to maximize the DC bus voltage utilization in [11].The difference between the output voltage amplitude and the maximum voltage amplitude of the inverter is regarded as the judgment of whether to enter the flux-weakening region to reduce the switching fluctuation caus...

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PMSM Model Predictive Control With Field-Weakening Implementation

Cited by 141 publications

References 28 publications

Single‐loop model prediction control of PMSM with moment of inertia identification

Single‐loop model prediction control of PMSM with moment of inertia identification

Nonlinear adaptive extended state space predictive control of permanent magnet synchronous motor

An Improved UDE-Based Flux-Weakening Control Strategy for IPMSM

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