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.

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.

Inverse system decoupling control strategy of BLIM based on stator flux orientation

2015 ◽  
Vol 48 (4) ◽  
pp. 469-480 ◽  
Author(s):  
Wenshao Bu ◽  
Chunxiao Lu ◽  
Conglin Zu ◽  
Haitao Zhang ◽  
Juanya Xiao
Keyword(s):  
Control Strategy ◽  
Inverse System ◽  
Decoupling Control ◽  
Stator Flux

Neural network inverse system decoupling control strategy of BLIM considering stator current dynamics

2018 ◽  
Vol 41 (3) ◽  
pp. 621-630 ◽  
Author(s):  
Wenshao Bu ◽  
Fangzhou He ◽  
Ziyuan Li ◽  
Haitao Zhang ◽  
Jingzhuo Shi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Strategy ◽  
Displacement Component ◽  
Inverse System ◽  
Decoupling Control ◽  
Motor Speed ◽  
State Equations ◽  
Stator Current ◽  
The Neural Network

The bearingless induction motor (BLIM) is a multi-variable, non-linear, strong coupling system. To achieve higher performance control, a novel neural network inverse system decoupling control strategy considering stator current dynamics is proposed. Taking the stator current dynamics of the torque windings into account, the state equations of the BLIM system is established first. Then, the inverse system model of the BLIM is identified by a three-layer neural network; by means of the neural network inverse system method, the BLIM system is decoupled into four independent second-order linear subsystems, include a rotor flux subsystem, a motor speed subsystem and two radial displacement component subsystems. On this basis, the neural network inverse decoupling control system is constructed, the simulation verification and analyses are performed. From the simulation results, it is clear that when the proposed decoupling control strategy is adopted, not only can the dynamic decoupling control between relevant variables be achieved, but the control system has a stronger anti-load disturbance ability, smaller overshoot and better tracking performance.

Sign in / Sign up

Export Citation Format

Share Document