New Search Algorithm of Model Predictive Control to Reducing Calculation Amount for Improving Steady Current Control Performance

2018 ◽  
Author(s):  
Masahiro Shimaoka ◽  
Shinji Doki
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Search Algorithm ◽  
Current Control ◽  
Control Performance ◽  
Steady Current

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 A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 760
Author(s):  
Fang Liu ◽  
Haotian Li ◽  
Ling Liu ◽  
Runmin Zou ◽  
Kangzhi Liu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Duty Cycle ◽  
Control Strategy ◽  
Disturbance Rejection ◽  
Torque Ripple ◽  
Current Control ◽  
Active Disturbance Rejection Control ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of the prediction model. Then, the dq-axis currents are selected by maximum torque per ampere (MTPA). Six virtual vectors are employed to FCSMPC, and a novel way to calculate the duty cycle is adopted. Finally, the simulation results show the validity and superiority of the proposed method.

Bilinear Model Predictive Control of Plasma Keyhole Pipe Welding Process

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Kun Qian ◽  
YuMing Zhang
Keyword(s):  
Model Predictive Control ◽  
Dynamic Model ◽  
Predictive Control ◽  
Search Algorithm ◽  
Welding Process ◽  
Nonlinear Process ◽  
Continuous Change ◽  
Bilinear Model ◽  
Peak Current ◽  
Pipe Welding

Controlled quasi-keyhole plasma arc welding process adjusts the amperage of the peak current to establish a keyhole in a desired time. This keyhole establishment time is the major parameter that controls the consistence of the weld penetration/quality and needs to be accurately controlled. This paper addresses the control of keyhole establishment time during pipe welding around the circumference, in which the gravitational force acting on the weld pool continuously changes. Because of this continuous change, the dynamic model of the controlled process, with the keyhole establishment time as the output and the amperage of the peak current as the input, varies around the circumference during welding. In addition, it is found that this dynamic model is nonlinear. To control this time varying nonlinear process, the authors propose an adaptive bilinear model predictive control (MPC) algorithm. A self-search algorithm is proposed to decouple the input and output in the model to apply the proposed MPC. Experiments confirmed the effectiveness of the developed control system including the adaptive bilinear MPC.

scholarly journals Disturbance-Observer-Based Model Predictive Control for Battery Energy Storage System Modular Multilevel Converters

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2285 ◽  
Author(s):  
Yantao Liao ◽  
Jun You ◽  
Jun Yang ◽  
Zuo Wang ◽  
Long Jin
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Current Control ◽  
Multilevel Converters ◽  
Battery Energy Storage ◽  
Circulating Current ◽  
Modular Multilevel Converters ◽  
Ac Current ◽  
Battery Energy

Although the traditional model predictive control (MPC) can theoretically provide AC current and circulating current control for modular multilevel converters (MMCs) in battery energy storage grid-connected systems, it suffers from stability problems due to the power quality of the power grid and model parameter mismatches. A two discrete-time disturbance observers (DOBs)-based MPC strategy is investigated in this paper to solve this problem. The first DOB is used to improve the AC current quality and the second enhances the stability of the circulating current control. The distortion and fluctuation of grid voltage and inductance parameter variation are considered as lump disturbances in the discrete modeling of a MMC. Based on the proposed method, the output prediction is compensated by disturbance estimation to correct the AC current and circulating current errors, which eventually achieve the expected tracking performance. Moreover, the DOBs have a quite low computational cost with minimum order and optimal performance properties. Since the designed DOBs work in parallel with the MPC, the control effect is improved greatly under harmonics, 3-phase unbalance, voltage sag, inductance parameter mismatches and power reversal conditions. Simulation results confirm the validity of the proposed scheme.

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