Current boundary based model predictive torque control of PMSM

In this paper, a current boundary based model predictive torque control is proposed to improve torque and flux control performance of permanent magnet synchronous motor (PMSM). To reduce torque and flux ripple, two voltage vectors are applied in one control period. Based on the current variations under the two vectors, torque is forced to reach a preset boundary at the end of a control period and can be limited to a band during the whole period. In addition, according to the predictive switching instants and the predictive current, some vector combinations can be excluded from the control set, which significantly reduces the computational burden of cost function evaluation. Simulation results reveal the effectiveness of the proposed strategy

An Improved Model Predictive Control Method to Drive an Induction Motor Fed by Three-Level Diode-Clamped Indirect Matrix Converter

In this paper, an improved model predictive control method is proposed to drive an induction motor fed by a three-level matrix converter. The main objective of this paper is to present a novel method to increase the switching frequency at a constant sampling time. Also, it is analytically discussed that increasing the switching frequency not only can decrease the motor current ripples, but it can also significantly reduce its torque ripples. Finally, this study demonstrates that reducing the motor current ripple will improve the quality of the supply current. To be the accurate model and validate the motor drive system, a co-simulation method, which is a combination of FLUX and MATLAB software packages, is employed to find the simulation results. The findings indicate that the proposed method diminishes the THD of the supply current up to 26% approximately. Furthermore, increasing the switching frequency results in the torque ripple reduction by up to 10% almost

Improved Model Predictive Current Control for SPMSM Drives With Parameter Mismatch

Model predictive current control (MPCC) can predict future motor behavior according to a motor model. In practice, however, motor parameters will vary at run time, and the parameter mismatch disturbances caused by the variation in motor parameters will deteriorate the MPCC performance. To suppress the parameter mismatch disturbances effectively, this paper proposes a modified MPCC with a current variation update mechanism. In contrast with the traditional current prediction equation that contains crude model parameters, the modified current prediction equation contains only measured information, taking advantage of the proposed current variation update mechanism, which can update the modified prediction equation within each sampling period. A simulation established by MATLAB software indicates that the proposed method can effectively suppress the parameter mismatch disturbances. Experiments are carried out to verify the correctness of the proposed method

Speed Sensorless Control of SPMSM Drives for EVs with a Binary Search Algorithm-Based Phase-Locked Loop

© 1967-2012 IEEE. This article presents a new method to extract accurate rotor position for the speed sensorless control of surface-mounted permanent-magnet synchronous motors (SPMSMs), based on the back electromotive force (EMF) information. The concept of finite control set-model predictive control is employed, and its cost function is related to the back EMF. An optimal voltage vector is selected from several given voltage vectors by comparing their fitness values. Moreover, the position space is divided into four sectors, and the fitness of each sector boundary is calculated and compared. The rotor position is first located in the sector surrounded by two boundaries that minimize the cost function. Then the selected sector is split into two parts, and the binary search algorithm is applied to reduce the sector area to improve the accuracy of position estimation. To overcome the drawback of the back EMF-based sensorless scheme, an I-f startup method is employed to accelerate the motor to the desired speed. An experiment has been carried out to compare the performance of the proposed method and the conventional phase-locked loop (PLL) in terms of steady-state and transient conditions

An Improved Model Predictive Current Control for PMSM Drives Based on Current Track Circle

Model predictive current control (MPCC) is a high-performance control strategy for permanent-magnet synchronous motor (PMSM) drives, with the features of quick response and simple computation. However, the conventional MPCC results in high torque and current ripples. This article proposes an improved MPCC scheme for PMSM drives. In the proposed scheme, the back electromotive force is estimated from the previous stator voltage and current, and it is used to predict the stator current for the next period. To further improve the steady state and dynamic performance, the proposed MPCC selects the optimal voltage vector based on a current track circle instead of a cost function. Compared with the calculation of cost function, the prediction of the current track circle is simple and quick. The proposed MPCC is compared with conventional MPCC and a duty-circle based MPCC by simulation and experiment in the aspect of converter output voltage and sensitivity analysis. Results prove the superiority of the proposed MPCC and its effectiveness in reducing the torque and current ripples of PMSM drives