Predictive Direct Torque Control ASIC with Fuzzy Voltage Vector Control and Neural Network PID Speed Controller

2021 ◽  
Author(s):  
Guo-Ming Sung ◽  
Ping-Yang Chiang ◽  
Yi-Yu Tsai
Keyword(s):  
Neural Network ◽  
Vector Control ◽  
Direct Torque Control ◽  
Torque Control ◽  
Speed Controller ◽  
Voltage Vector ◽  
Neural Network Pid

scholarly journals Direct Torque Control with Adaptive PI Speed Controller based on Neural Network for PMSM Drives

2018 ◽  
Vol 160 ◽  
pp. 02011
Author(s):  
Jia, Zhenyu ◽  
Kim, Byeongwoo
Keyword(s):  
Neural Network ◽  
Permanent Magnet Synchronous Motor ◽  
Direct Torque Control ◽  
Back Propagation ◽  
Torque Control ◽  
Fast Speed ◽  
Speed Controller ◽  
Intelligent Technique ◽  
Artificial Intelligent Technique ◽  
Speed Response

This paper presents an adaptive speed controller based on artificial intelligent technique to improvethe performance of classical Direct Torque Control (DTC) for Permanent Magnet Synchronous Motor (PMSM) drives. The proposed method applies back propagation (BP) based neural network (NN) to tune the parameters of classical proportional-integral (PI) speed controller. Comparisons between conventional PI speed controller and proposed method are carried out by Simulation.Simulation results demonstrate that conventional DTC system based on the proposed NN speed controller can achieve higher performance with fast speed response, small overshoot and robustness.

scholarly journals Direct Torque Control of a Small Wind Turbine with a Sliding-Mode Speed Controller

2016 ◽  
Vol 753 ◽  
pp. 052031 ◽  
Author(s):  
Jagath Sri Lal Senanayaka ◽  
Hamid Reza Karimi ◽  
Kjell G. Robbersmyr
Keyword(s):  
Wind Turbine ◽  
Sliding Mode ◽  
Direct Torque Control ◽  
Torque Control ◽  
Small Wind Turbine ◽  
Speed Controller

scholarly journals Exponential Reaching Law and Sensorless DTC IM Control with Neural Network Online Parameters Estimation based on MRAS

2018 ◽  
Vol 7 (2) ◽  
pp. 77
Author(s):  
Legrioui Said ◽  
Rezgui Salah Eddine ◽  
Benalla Hocine
Keyword(s):  
Neural Network ◽  
Time Constant ◽  
Sliding Mode ◽  
Direct Torque Control ◽  
Parameters Estimation ◽  
Torque Control ◽  
System Control ◽  
Reaching Law ◽  
Exponential Reaching Law ◽  
Stator Resistance

The most important problem in the control of induction machine (IM) is the change of its parameters, especially the stator resistance and rotor-time constant. The objective of<em> </em>this paper is to implement a new strategy in sensorless direct torque control (DTC) of an IM drive. The rotor flux based model reference adaptive system (MRAS) is used<em> </em>to estimate conjointly<em> </em>the rotor<em> </em>speed, the stator resistance and the inverse rotor time constant, the process of the estimation is performed on-line by a new MRAS-based artificial neural network (ANN) technique. Furthermore, the drive is complemented with a new exponential reaching law (ERL), based on the sliding mode control (SMC) to significantly improve the performances of the system control compared to the conventional SMC which is known to be susceptible to the annoying chattering phenomenon. An experimental investigation was carried out via the Matlab/Simulink with real time interface (RTI) and dSPACE (DS1104) board where the behavior of the proposed method was tested at different points of IM operation.

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