The Decoupling Control of Induction Motor Based on Artificial Neural Network Inverse System Method

2010 ◽  
Author(s):  
Yongxian Song ◽  
Juanli Ma ◽  
Hanxia Zhang ◽  
Naibao He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Inverse System ◽  
Decoupling Control ◽  
Inverse System Method ◽  
System Method ◽  
Artificial Neural

Decoupling Control Strategy of Three-Phase Bearingless Induction Motor

2013 ◽  
Vol 433-435 ◽  
pp. 1154-1160
Author(s):  
Wen Shao Bu ◽  
Cong Lin Zu ◽  
Chun Xiao Lu ◽  
Xin Wen Niu
Keyword(s):  
Induction Motor ◽  
Control Strategy ◽  
Inverse System ◽  
Decoupling Control ◽  
Inverse System Method ◽  
Object A ◽  
System Method ◽  
Three Phase ◽  
Nonlinear Object ◽  
Rotor Flux

For the strong coupling problem of three-phase bearingless induction motor which is a multi- variable and nonlinear object, a kind of decoupling control strategy based on inverse system method is proposed. The reversibility of torque subsystem was analyzed based on rotor flux orientation, and the decoupling control strategy based on inverse system method was analyzed. Then the torque system was decoupled into two second-order linear subsystems, i.e. the rotor speed subsystems and the rotor flux subsystems. The suspension system adopts negative feedback control; the required air-gap flux linkage of torque system was obtained from the rotor flux and stator current. Finally, synthesis and simulation of the overall control system were researched. Simulation results demonstrate that good performance of decoupling control can be achieved. The presented control strategy is feasible and available.

scholarly journals Inverse System Analysis and Modeling of Bearingless Induction Motor and Its Combined Control Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wen-shao Bu ◽  
Cong-lin Zu ◽  
Chun-xiao Lu
Keyword(s):  
Induction Motor ◽  
Strong Coupling ◽  
Control Strategy ◽  
Inverse System ◽  
Decoupling Control ◽  
Flux Linkage ◽  
Inverse System Method ◽  
Stator Current ◽  
System Method ◽  
Stator Flux

Bearingless induction motor is a multi-variable, nonlinear and strong coupling object, the existing inverse control method ignores the stator current dynamics of torque system. Aiming at its nonlinear and strong coupling problems, a novel combinatorial decoupling control strategy based on stator flux orientation and inverse system method is proposed. Taking the stator current dynamics of four-pole torque system into account, the reversibility and inverse system model of torque system are analyzed and established. Adopting the inverse system method, the dynamic decoupling between motor speed and stator flux-linkage is achieved; by online identification and calculation, the airgap flux-linkage of torque system is got. Based on above, feedback and compensation control of two radial displacement components of two-pole suspension system is realized. Simulation results have shown the higher decoupling control performance and stronger anti-interference ability of the decoupling control system; the proposed decoupling strategy not only owns the characteristics of be simple and convenient, but also is effective and feasible.

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

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