scholarly journals An Improved Model-Free Current Predictive Control Method for SPMSM Drives

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 134672-134681
Author(s):  
Xuerong Li ◽  
Yang Wang ◽  
Xingzhong Guo ◽  
Xing Cui ◽  
Shuo Zhang ◽  
...  
Keyword(s):  
Predictive Control ◽  
Control Method ◽  
Model Free ◽  
Free Current ◽  
Improved Model

scholarly journals Improved Model-Free Adaptive Sliding-Mode-Constrained Control for Linear Induction Motor considering End Effects

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoqi Song ◽  
Dezhi Xu ◽  
Weilin Yang ◽  
Yan Xia ◽  
Bin Jiang
Keyword(s):  
Control Method ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Research Field ◽  
Constrained Control ◽  
End Effects ◽  
Linear Induction ◽  
Adaptive Sliding Mode ◽  
Model Free ◽  
Improved Model

As a kind of special motors, linear induction motors (LIM) have been an important research field for researchers. However, it gives a great velocity control challenge due to the complex nonlinearity, high coupling, and unique end effects. In this article, an improved model-free adaptive sliding-mode-constrained control method is proposed to deal with this problem dispensing with internal parameters of the LIM. Firstly, an improved compact form dynamic linearization (CFDL) technique is used to simplify the LIM plant. Besides, an antiwindup compensator is applied to handle the problem of the actuator under saturations in case during the controller design. Furthermore, the stability of the closed system is proved by Lyapunov stability method theoretically. Finally, simulation results are given to demonstrate that the proposed controller has excellent dynamic performance and stronger robustness compared with traditional PID controller.

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

2020 ◽  
Vol 10 (4) ◽  
pp. 265-279
Author(s):  
Arman Farhadi ◽  
Amir Akbari ◽  
Ali Zakerian ◽  
Mohammad Tavakoli Bina
Keyword(s):  
Model Predictive Control ◽  
Induction Motor ◽  
Predictive Control ◽  
Control Method ◽  
Torque Ripple ◽  
Matrix Converter ◽  
Switching Frequency ◽  
Motor Current ◽  
Supply Current ◽  
Improved Model

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.

scholarly journals Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2325
Author(s):  
Sanaz Sabzevari ◽  
Rasool Heydari ◽  
Maryam Mohiti ◽  
Mehdi Savaghebi ◽  
Jose Rodriguez
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Strategies ◽  
Power Converter ◽  
Performance Criterion ◽  
Parameter Mismatch ◽  
Model Free ◽  
Finite Control ◽  
Free Current ◽  
Definition Of

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.

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