Two-Vector-Based Model Predictive Torque Control Without Weighting Factors for Induction Motor Drives

Zhang, Yongchang; Yang, Haojin

doi:10.1109/tpel.2015.2416207

Cited by 369 publications

(205 citation statements)

References 29 publications

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“…In the last years, model predictive control (MPC) is perceived as a favorable alternative to those both control methods. MPC technique has been applied to numerous types of motors, comprehensive induction machines [8], permanent magnet synchronous machines (PMSMs) [9], interior PMSMs [10], brush-less DC motors [11], and synchronous reluctance motor [12].…”

Section: Introductionmentioning

confidence: 99%

Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

et al. 2020

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In this paper, a simplified efficient method for sensorless finite set current predictive control (FSCPC) for synchronous reluctance motor (SynRM) based on extended Kalman filter (EKF) is proposed. The proposed FSCPC is based on reducing the computation burden of the conventional FSCPC by using the commanded reference currents to directly calculate the reference voltage vector (RVV). Therefore, the cost function is calculated for only three times and the necessity to test all possible voltage vectors will be avoided. For sensorless control, EKF is composed to estimate the position and speed of the rotor. Whereas the performance of the proposed FSCPC essentially necessitates the full knowledge of SynRM parameters and provides an insufficient response under the parameter mismatch between the controller and the motor, online parameter estimation based on EKF is combined in the proposed control strategy to estimate all parameters of the machine. Furthermore, for simplicity, the parameters of PI speed controller and initial values of EKF covariance matrices are tuned offline using Particle Swarm Optimization (PSO). To demonstrate the feasibility of the proposed control, it is implemented in MATLAB/Simulink and tested under different operating conditions. Simulation results show high robustness and reliability of the proposed drive.

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Section: Introductionmentioning

confidence: 99%

Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

et al. 2020

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“…With the discrete nature of the voltage source inverter and the limited number of voltage vectors, FCS-MPC has gained much attention among the proposed strategies. Many scholars have already successfully used this method to control the flux and torque in induction machines (IMs) and permanent magnet synchronous motors (PMSMs) [16][17][18][19][20]. There are also some studies in which the MPC strategy is used in SRM [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Flux linkage optimization for direct torque control of switched reluctance motor based on model predictive control

Shang

Huang

et al. 2019

IEEJ Transactions Elec Engng

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In this paper, to solve the problem of large torque ripple in a switched reluctance motor (SRM), a new control strategy based on model predictive flux control is proposed in the direct torque control (DTC) algorithm. First, the flux linkage value in the next period is calculated from the discrete time model of the SRM. Second, to reduce the computation burden and increase the dynamic response, a torque hysteresis controller is embedded in the system to select three candidate voltage vectors for the cost function. Finally, based on flux linkage minimization, the best voltage vector is selected and applied to the system. The proposed direct torque and predictive flux control (DTPFC) method perfectly suits the nonlinear magnetic characteristics of SRM, and shows better steady‐state and dynamic‐state performances compared to DTC. Furthermore, since DTPFC has only one variable in the cost function, the huge task of obtaining the best weighting factor is eliminated. Simulation and experiments are carried out on a three‐phase 12/8 SRM with the proposed control algorithm and DTC. The results demonstrate the validity of the new control approach. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…When MPC is applied to the motor drives for the aim of torque control, it is usually called model predictive torque control (MPTC), which has been considered as an effective alternative to conventional DTC [21]- [29]. Different from conventional table-based DTC, MPTC uses online optimization to replace the heuristic switching table.…”

Section: Introductionmentioning

confidence: 99%

Model-Predictive Control of Induction Motor Drives: Torque Control Versus Flux Control

Zhang

Yang

Xia

2016

IEEE Trans. on Ind. Applicat.

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147

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Recently, model predictive torque control (MPTC) has been introduced as a powerful control method for induction motor (IM) drives. However, the weighting factor for stator flux must be tuned carefully to obtain satisfactory performance at different operation points. Unfortunately, so far the tuning of weighting factor in MPTC is mostly based on empirical procedure. This paper solves this problem by proposing a model predictive flux control (MPFC), which uses the stator flux vector as the control variable and eliminates the complicated prediction of stator current at k+2 instant. As a result, the weighting factor in conventional MPTC is eliminated and the control complexity is significantly reduced. Both MPTC and MPFC are tested and compared in detail, including steady state performance, dynamic response and low speed operation. The simulation and experimental results prove that, the performance of conventional MPTC is dependent on the weighting factor and improper weighting factor would lead to significant performance deterioration. On the contrary, the proposed MPFC achieves similar or even better overall performance over a wide speed range with very low tuning work. Hence, it is concluded that the proposed MPFC is more practical than conventional MPTC.

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Two-Vector-Based Model Predictive Torque Control Without Weighting Factors for Induction Motor Drives

Cited by 369 publications

References 29 publications

Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

Flux linkage optimization for direct torque control of switched reluctance motor based on model predictive control

Model-Predictive Control of Induction Motor Drives: Torque Control Versus Flux Control

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