scholarly journals Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2051 ◽  
Author(s):  
Jian Li ◽  
Xiaoyan Huang ◽  
Feng Niu ◽  
Chaojie You ◽  
Lijian Wu ◽  
...  
Keyword(s):  
Motor Control ◽  
Theoretical Analysis ◽  
Prediction Model ◽  
Prediction Error ◽  
Current Control ◽  
Experimental Results ◽  
Control Performance ◽  
Parameter Mismatch ◽  
Finite Control ◽  
Control Set

Finite-control-set model predictive current control (FCS-MPCC) has been widely investigated in the field of motor control. When the discrete motor prediction model is not obtained accurately, prediction error often occurs, which can result in improper determinations of optimal voltage vectors and can further affect the control performance of motor systems. However, papers evaluating the motor control performance employing FCS-MPCC rarely consider prediction error and its utilization to weaken the influence of inaccurate prediction model. This paper investigates in depth the prediction error caused by three influencing factors from the perspective of model accuracy—discretization method, prediction stepsize, and parameter mismatch. Firstly, the evaluation index, prediction error, is defined and its formulas considering the above three factors are derived based on interior permanent magnet synchronous motor (IPMSM). Then, the theoretical analysis of prediction error is provided. Finally, experimental results of an IPMSM drive system are presented to verify and complement the theoretical analysis. Both the theoretical analysis and experimental results fully elaborate the prediction error, which can offer practical guidelines for the evaluation and improvement of motor control performance, especially for FCS-MPCC in IPMSM applications.

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

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6342
Author(s):  
Zehao Lyu ◽  
Xiang Wu ◽  
Jie Gao ◽  
Guojun Tan
Keyword(s):  
Control Method ◽  
Current Control ◽  
Parameter Mismatch ◽  
Robust Model ◽  
Control Robustness ◽  
Interior Permanent Magnet ◽  
Finite Control ◽  
Control Set ◽  
The Cost ◽  
The Mathematical Model

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.

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

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianfeng Yang ◽  
Yang Liu ◽  
Rui Yan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Parameter Uncertainty ◽  
Prediction Error ◽  
Continuous Control ◽  
Power Electronic ◽  
Parameter Mismatch ◽  
Three Phase ◽  
Finite Control ◽  
Control Set

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.

scholarly journals Robust Perturbation Observer-based Finite Control Set Model Predictive Current Control for SPMSM Considering Parameter Mismatch

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3711 ◽  
Author(s):  
Liu ◽  
Zhao
Keyword(s):  
Current Control ◽  
Parameter Uncertainties ◽  
Permanent Magnet Synchronous Motors ◽  
Machine Model ◽  
Parameter Mismatch ◽  
Servo Systems ◽  
Finite Control ◽  
Control Situations ◽  
Control Set ◽  
The Stability

In order to improve the dynamics of the surface-mounted permanent magnet synchronous motors (SPMSM) used in servo systems, finite control set model predictive current control (FCS-MPCC) methods have been widely adopted. However, because the FCS-MPCC is a model-based strategy, its performance highly depends on the machine parameters, such as the winding resistance, inductance and flux linkage. Unfortunately, the parameter mismatch problem is common due to the measurement precision and environmental impacts (e.g., temperature). To enhance the robustness of the SPMSM FCS-MPCC systems, this paper proposes a Lundberg perturbation observer that is seldom used in the FCS model predictive control situations to remove the adverse effects caused by resistance and inductance mismatch. Firstly, the system model is established, and the FCS-MPCC mechanism is illustrated. Based on the machine model, the sensitivity of the control algorithm to the parameter mismatch is discussed. Then, the Luenberger perturbation observer that can estimate the general disturbance arising from the parameter uncertainties is developed, and the stability of the observer is analyzed by using the discrete pole assignment technique. Finally, the proposed disturbance observer is incorporated into the FCS-MPCC prediction plant model for real-time compensation. Both simulation and experiments are conducted on a three-phase SPMSM, verifying that the proposed strategy has marked control performance and strong robustness.

scholarly journals Continuous Control Set Predictive Current Control for Induction Machine

2021 ◽  
Vol 11 (13) ◽  
pp. 6230
Author(s):  
Toni Varga ◽  
Tin Benšić ◽  
Vedrana Jerković Štil ◽  
Marinko Barukčić
Keyword(s):  
Control Method ◽  
Induction Machine ◽  
Current Control ◽  
Continuous Control ◽  
Loop Model ◽  
Voltage Reference ◽  
Control Loop ◽  
Speed Tracking ◽  
Finite Control ◽  
Control Set

A speed tracking control method for induction machine is shown in this paper. The method consists of outer speed control loop and inner current control loop. Model predictive current control method without the need for calculation of the weighing factors is utilized for the inner control loop, which generates a continuous set of voltage reference values that can be modulated and applied by the inverter to the induction machine. Interesting parallels are drawn between the developed method and state feedback principles that helped with the analysis of the stability and controllability. Simple speed and rotor flux estimator is implemented that helps achieve sensorless control. Simulation is conducted and the method shows great performance for speed tracking in a steady state, and during transients as well. Additionally, compared to the finite control set predictive current control, it shows less harmonic content in the generated torque on the rotor shaft.

scholarly journals Finite control set model predictive direct current control strategy with constraints applying to drive three-phase induction motor

2021 ◽  
Vol 11 (4) ◽  
pp. 2916
Author(s):  
Anmar Kh. Ali ◽  
Riyadh G. Omar
Keyword(s):  
Induction Motor ◽  
Direct Current ◽  
Control Strategy ◽  
System Performance ◽  
Torque Ripple ◽  
Current Control ◽  
Three Phase ◽  
Direct Current Control ◽  
Finite Control ◽  
Control Set

In this, work the finite control set (FCS) model predictive direct current control strategy with constraints, is applied to drive three-phase induction motor (IM) using the well-known field-oriented control. As a modern algorithm approach of control, this kind of algorithm decides the suitable switching combination that brings the error between the desired command currents and the predicated currents, as low as possible, according to the process of optimization. The suggested algorithm simulates the constraints of maximum allowable current and the accepted deviation, between the desired command and actual currents. The new constraints produce an improvement in system performance, with the predefined error threshold. This can be applied by avoiding the switching combination that exceeds the limited values. The additional constraints are more suitable for loads that require minimum distortion in harmonic and offer protection from maximum allowable currents. This approach is valuable especially in electrical vehicle (EV) applications since its result offers more reliable system performance with low total harmonics distortion (THD), low motor torque ripple, and better speed tracking.

A Novel Finite-Control-Set Model Predictive Current Control for Five-Phase PM Motor With Continued Modulation

2020 ◽  
Vol 35 (7) ◽  
pp. 7261-7270 ◽  
Author(s):  
Wenxiang Zhao ◽  
Tao Tao ◽  
Jihong Zhu ◽  
Huajun Tan ◽  
Yuxuan Du
Keyword(s):  
Current Control ◽  
Finite Control ◽  
Control Set
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