The Neural Network Inverse Control Method of Induction Motor Based on Multiscalar Model

2010 ◽  
Author(s):  
Xin Wang ◽  
Yaoming Zhang ◽  
Liguo Sun ◽  
Xiang Diao
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Control Method ◽  
Inverse Control ◽  
The Neural Network

scholarly journals The Neural Network-Combined Optimal Control System of Induction Motor

2019 ◽  
Vol 9 (4) ◽  
pp. 2513
Author(s):  
Thang Nguyen Trong
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Control System ◽  
Induction Motor ◽  
Control Method ◽  
Response Speed ◽  
The State ◽  
Linear Quadratic ◽  
Neuron Network ◽  
The Neural Network

<span lang="EN-US">This research aims to propose the optimal control method combined with the neuron network for an induction motor. In the proposed system, the induction motor is a nonlinear object which is controlled at each working point. At these working-points, the state equation of the induction motor is linear, so it is possible to apply the linear quadratic regular algorithm for the induction motor. Therefore, the parameters of the state feedback controller are the functions. The output-input relationships of these functions are set through the neural network. The numerical simulation results show that the quality of the control system of the induction motor is very high: The response speed always follows the desired speed with the short transition time and the small overshoot. Furthermore, the system is robust in the case of changing the load torque, and the parameters of the induction motor are incorrectly defined</span>

scholarly journals Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4946
Author(s):  
Tuan Pham Van ◽  
Dung Vo Tien ◽  
Zbigniew Leonowicz ◽  
Michal Jasinski ◽  
Tomasz Sikorski ◽  
...  
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Learning Rate ◽  
Accurate Estimation ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural ◽  
Stator Resistance

This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

scholarly journals Neural networks-based robust adaptive flight path tracking control of large transport

2018 ◽  
Vol 38 (3) ◽  
pp. 268-278
Author(s):  
Maolong Lv ◽  
Xiuxia Sun ◽  
G. Z. Xu ◽  
Z. T. Wang
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Dynamic Surface Control ◽  
Continuous Functions ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
The Neural Network ◽  
Neural Network Dynamic ◽  
Robust Adaptive

For the ultralow altitude airdrop decline stage, many factors such as actuator nonlinearity, the uncertain atmospheric disturbances, and model unknown nonlinearity affect the precision of trajectory tracking. A robust adaptive neural network dynamic surface control method is proposed. The neural network is used to approximate unknown nonlinear continuous functions of the model, and a nonlinear robust term is introduced to eliminate the actuator’s nonlinear modeling error and external disturbances. From Lyapunov stability theorem, it is rigorously proved that all the signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.

scholarly journals Artificial Neural Network Application for Current Sensors Fault Detection in the Vector Controlled Induction Motor Drive

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 571 ◽  
Author(s):  
Mateusz Dybkowski ◽  
Kamil Klimkowski
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Control Algorithm ◽  
Control Structure ◽  
Fault Tolerant ◽  
Current Sensor ◽  
Sensor Faults ◽  
Safety Level ◽  
The Neural Network ◽  
Network Application

This paper describes a Fault Tolerant Control structure for the Induction Motor (IM) drive. We analyzed the influence of current sensor faults on the properties of the vector-controlled IM drive system. As a control algorithm, the Direct Field Oriented Control structure was chosen. For the proper operation of this system and for other vector algorithms, information about the stator currents components is required. It is important to monitor and detect these sensor faults, especially in drives with an increased safety level. We discuss the possibility of the neural network application in detecting stator current sensor faults in the vector control algorithm. Simulation and experimental results for various drive conditions are presented.

Neural Network-Based Speed Control of A Two-Mass-Model System

1999 ◽  
Vol 3 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Rached Dhaouadi ◽  
◽  
Khaled Nouri
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Mass Model ◽  
Neural Network Learning ◽  
Inverse Control ◽  
Network Learning ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Neural Network Structure

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

Adaline neural network-based adaptive inverse control for an electro-hydraulic servo system

2011 ◽  
Vol 17 (13) ◽  
pp. 2007-2014 ◽  
Author(s):  
Jianjun Yao ◽  
Xiancheng Wang ◽  
Shenghai Hu ◽  
Wei Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Servo System ◽  
Inverse Model ◽  
Lms Algorithm ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
The Neural Network ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

Based on adaptive inverse control theory, combined with neural network, neural network adaptive inverse controller is developed and applied to an electro-hydraulic servo system. The system inverse model identifier is constructed by neural network. The task is accomplished by generating a tracking error between the input command signal and the system response. The weights of the neural network are updated by the error signal in such a way that the error is minimized in the sense of mean square using (LMS) algorithm and the neural network is close to the system inverse model. The above steps make the gain of the serial connection system close to unity, realizing waveform replication function in real-time. To enhance its convergence and robustness, the normalized LMS algorithm is applied. Simulation in which nonlinear dead-zone is considered and experimental results demonstrate that the proposed control scheme is capable of tracking desired signals with high accuracy and it has good real-time performance.

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