scholarly journals Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1727
Author(s):  
Ibrahim Farouk Bouguenna ◽  
Ahmed Tahour ◽  
Ralph Kennel ◽  
Mohamed Abdelrahem
Keyword(s):  
Fuzzy Logic ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Drive ◽  
Fuzzy Logic Controller ◽  
Sampling Period ◽  
Vector Model ◽  
Voltage Vector ◽  
Drive Systems ◽  
Sampling Instant

This article presents a multiple-vector finite-control-set model predictive control (MV-FCS-MPC) scheme with fuzzy logic for permanent-magnet synchronous motors (PMSMs) used in electric drive systems. The proposed technique is based on discrete space vector modulation (DSVM). The converter’s real voltage vectors are utilized along with new virtual voltage vectors to form switching sequences for each sampling period in order to improve the steady-state performance. Furthermore, to obtain the reference voltage vector (VV) directly from the reference current and to reduce the calculation load of the proposed MV-FCS-MPC technique, a deadbeat function (DB) is added. Subsequently, the best real or virtual voltage vector to be applied in the next sampling instant is selected based on a certain cost function. Moreover, a fuzzy logic controller is employed in the outer loop for controlling the speed of the rotor. Accordingly, the dynamic response of the speed is improved and the difficulty of the proportional-integral (PI) controller tuning is avoided. The response of the suggested technique is verified by simulation results and compared with that of the conventional FCS-MPC.

scholarly journals An MPC Scheme with Enhanced Active Voltage Vector Region for V2G Inverter

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1312
Author(s):  
Lie Xia ◽  
Lianghui Xu ◽  
Qingbin Yang ◽  
Feng Yu ◽  
Shuangqing Zhang
Keyword(s):  
Steady State ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Harmonic Distortion ◽  
Sampling Period ◽  
Duty Ratio ◽  
Output Current ◽  
Vehicle To Grid ◽  
Voltage Vector ◽  
Steady State Performance

In this paper, a model predictive control (MPC) scheme with an enhanced active voltage vector region (AV2R) was developed and implemented to achieve better steady-state performance and lower total harmonic distortion (THD) of the output current for a vehicle-to-grid (V2G) inverter. Firstly, the existing MPC methods conducted with single vector and two vectors during one sampling period were analyzed and the corresponding AV2Rs were elaborated. Secondly, the proposed strategy was investigated, aiming at expanding the AV2R and improving the steady-state performance accordingly. A formal mathematical methodology was studied in terms of duty ratio calculation. Lastly, the proposed method was carried out through experimentation. For comparison, the experimental results of the three mentioned methods were provided as well, proving the effectiveness of the proposed algorithm.

scholarly journals Three-Vector-Based Low Complexity Model Predictive Control for Soft Open Point

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Zhengqi Wang ◽  
Haoyu Zhou ◽  
Qunhai Huo ◽  
Sipeng Hao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Low Complexity ◽  
Sampling Time ◽  
Vector Model ◽  
Dynamic State ◽  
Network Systems ◽  
Voltage Vector ◽  
Open Point

Soft open point (SOP) can improve the flexibility and reliability of power supplies; thus, they are widely used in distribution network systems. Traditional single-vector model predictive control (SV-MPC) can quickly and flexibly control the power and current at both ports of the SOP. However, SV-MPC can only select one voltage vector in a sampling time, producing large current ripples, and power fluctuations. In order to solve the above problems, this paper proposes a three-vector-based low complexity model predictive control (TV-MPC). In the proposed control method, two effective voltage vectors and one zero voltage vector are selected in a sampling time. For the two-port SOP, methods are given to judge the sectors on both sides and select the voltage vectors. Furthermore, the calculation method of the distribution time is proposed as well. Finally, the effectiveness of the proposed method is verified by steady-state and dynamic-state simulation results compared with the SV-MPC.

scholarly journals Multi-Virtual-Vector Model Predictive Current Control for Dual Three-Phase PMSM

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7292
Author(s):  
Tianjiao Luan ◽  
Zhichao Wang ◽  
Yang Long ◽  
Zhen Zhang ◽  
Qi Li ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Current Control ◽  
Vector Model ◽  
Permanent Magnet Synchronous Machine ◽  
Control Performance ◽  
Voltage Vector ◽  
Three Phase ◽  
Tracking Ability ◽  
Zero Vector

This paper proposes a multi-virtual-vector model predictive control (MPC) for a dual three-phase permanent magnet synchronous machine (DTP-PMSM), which aims to regulate the currents in both fundamental and harmonic subspace. Apart from the fundamental α-β subspace, the harmonic subspace termed x-y is decoupled in multiphase PMSM according to vector space decomposition (VSD). Hence, the regulation of x-y currents is of paramount importance to improve control performance. In order to take into account both fundamental and harmonic subspaces, this paper presents a multi-virtual-vector model predictive control (MVV-MPC) scheme to significantly improve the steady performance without affecting the dynamic response. In this way, virtual vectors are pre-synthesized to eliminate the components in the x-y subspace and then a vector with adjustable phase and amplitude is composed of two effective virtual vectors and a zero vector. As a result, an enhanced current tracking ability is acquired due to the expanded output range of the voltage vector. Lastly, both simulation and experimental results are given to confirm the feasibility of the proposed MVV-MPC for DTP-PMSM.

scholarly journals Reduction of Prediction Errors for the Matrix Converter with an Improved Model Predictive Control

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 3029 ◽  
Author(s):  
Shuang Feng ◽  
Chaofan Wei ◽  
Jiaxing Lei
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Models ◽  
Sampling Period ◽  
Continuous Model ◽  
Matrix Converter ◽  
Prediction Errors ◽  
Current Harmonics ◽  
The Matrix ◽  
Improved Model

In this paper, an improved model predictive control (MPC) is proposed for the matrix converter (MC). First, the conventional MPC which adopts the separately discretized prediction models is discussed. It shows that the conventional MPC ignores the input–output interaction in every sampling period. Consequently, additional prediction errors arise, resulting in more current harmonics. Second, the principle of the improved MPC is presented. With the interaction considered, the integral state-space equation of the whole MC system is constructed and discretized to obtain the precise model. The eigenvalue analysis shows that the proposed prediction model has the same eigenvalues with the continuous model, and thus is more accurate than the conventional one to describe the MC’s behavior in every sampling period. Finally, experimental results under various working conditions prove that the proposed approach can always increase the control accuracy and reduce the harmonic distortions, which in turn requires smaller filter components.

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