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 A finite set-model predictive control based on FPGA flatform for eleven-level cascaded H-Bridge inverter fed induction motor drive

2021 ◽  
Vol 12 (2) ◽  
pp. 845
Author(s):  
Mai Van Chung ◽  
Do Tuan Anh ◽  
Phuong Vu
Keyword(s):  
Model Predictive Control ◽  
Induction Motor ◽  
Predictive Control ◽  
Control Method ◽  
Dynamic Responses ◽  
Delay Compensation ◽  
Voltage Vector ◽  
Finite Control ◽  
Finite Set ◽  
The Cost

Model predictive control has been considered as a powerful alternative control method in power converters and electrical drives recently. This paper proposes a novel method for finite control set predictive control algorithm foran induction motor fed by 11-level cascaded H-Bridge converter. To deal with the high computation volume of MPC algorithm applied for CHBconverter, 7-adjacent vectors method is applied for calculating the desired voltage vector which minimizes the cost function. Moreover, by utilizingfield programmable gate array (FPGA) platform with its flexible structure,the total execution time reduces considerably so that the selected voltage vector can be applied immediately without delay compensation. This method improves the dynamic responses and steady-state performance of the system. Finally, experimental results verify the effectiveness of control design

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 Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Aerospace ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
Kaiyang Guo ◽  
Pan Tang ◽  
Hui Wang ◽  
Defu Lin ◽  
Xiaoxi Cui
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Planning Process ◽  
Control Method ◽  
External Disturbance ◽  
Sampling Time ◽  
External Disturbances ◽  
Autonomous Landing ◽  
Moving Platform ◽  
Landing Trajectory

Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.

scholarly journals Common-Mode Voltage Reduction Algorithm for Photovoltaic Grid-Connected Inverters with Virtual-Vector Model Predictive Control

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2607
Author(s):  
Hui Hwang Goh ◽  
Xinyi Li ◽  
Chee Shen Lim ◽  
Dongdong Zhang ◽  
Wei Dai ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pulse Width Modulation ◽  
Control Method ◽  
Dynamic Control ◽  
Single Stage ◽  
Control Technique ◽  
Vector Model ◽  
Common Mode ◽  
Common Mode Voltage

Model predictive control (MPC) has been proven to offer excellent model-based, highly dynamic control performance in grid converters. The increasingly higher power capacity of a PV inverter has led to the industrial preference of adopting higher DC voltage design at the PV array (e.g., 750–1500 V). With high array voltage, a single stage inverter offers advantages of low component count, simpler topology, and requiring less control tuning effort. However, it is typically entailed with the issue of high common-mode voltage (CMV). This work proposes a virtual-vector model predictive control method equipped with an improved common-mode reduction (CMR) space vector pulse width modulation (SVPWM). The modulation technique essentially subdivides the hexagonal voltage vector space into 18 sub-sectors, that can be split into two groups with different CMV properties. The proposal indirectly increases the DC-bus utilization and extends the overall modulation region with improved CMV. The comparison with the virtual-vector MPC scheme equipped with the conventional SVPWM suggests that the proposed technique can effectively suppress 33.33% of the CMV, and reduce the CMV toggling frequency per fundamental cycle from 6 to either 0 or 2 (depending on which sub-sector group). It is believed that the proposed control technique can help to improve the performance of photovoltaic single-stage inverters.

A Dual-vector Model Predictive Control Method With Minimum Current THD

2021 ◽  
pp. 1-1
Author(s):  
Xu Zhang ◽  
Xiang Wu ◽  
Guojun Tan ◽  
Weifeng Zhang ◽  
Qiang Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Vector Model ◽  
Dual Vector

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.

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