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.

Decoupling Control of the Gas Collectors’ Pressure System Based on Neural Network Inverse System

2014 ◽  
Vol 530-531 ◽  
pp. 985-989
Author(s):  
Fan Wei Meng ◽  
Qing Tian ◽  
Bin Xu
Keyword(s):  
Neural Network ◽  
Composite System ◽  
Nonlinear Approximation ◽  
Original System ◽  
Inverse System ◽  
Decoupling Control ◽  
Pressure System ◽  
Approximate Inverse ◽  
The Neural Network ◽  
The Mathematical Model

For collectors’ pressure system strong interference, coupled, nonlinear, multi-parameter and other characteristics, based on the inverse system decoupling principle, the reversibility of the mathematical model of the gas collectors’ pressure system is analyzed. BP neural network which has strong nonlinear approximation ability is applied, to approximate inverse system of gas collectors’ pressure system. Neural network inverse system with the original system composes of the pseudo linear decoupling composite system. The neural network inverse decoupling control of gas collectors’ pressure system is implemented. The simulation results show that this method realizes decoupling, has a certain application.

Decoupling Control of the Gas Collectors' Pressure System Based on Neural Network Inverse System

2013 ◽  
Vol 457-458 ◽  
pp. 888-892
Author(s):  
Qing Tian ◽  
Jie Sun
Keyword(s):  
Neural Network ◽  
Composite System ◽  
Nonlinear Approximation ◽  
Original System ◽  
Inverse System ◽  
Decoupling Control ◽  
Pressure System ◽  
Approximate Inverse ◽  
The Neural Network ◽  
The Mathematical Model

For collectors pressure system strong interference, coupled, nonlinear, multi-parameter and other characteristics, based on the inverse system decoupling principle, the reversibility of the mathematical model of the gas collectors pressure system is analyzed. BP neural network which has strong nonlinear approximation ability is applied, to approximate inverse system of gas collectors pressure system. Neural network inverse system with the original system composes of the pseudo linear decoupling composite system. The neural network inverse decoupling control of gas collectors pressure system is implemented. The simulation results show that this method realizes decoupling, has a certain application.

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.

scholarly journals Decoupling Control Strategy of BLIM Based on Neural Network Inverse System

2017 ◽  
Vol 10 (1) ◽  
pp. 85-98 ◽  
Author(s):  
Wenshao Bu ◽  
Ziyuan Li ◽  
Juanya Xiao ◽  
Xiaoqiang Li
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Inverse System ◽  
Decoupling Control

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

scholarly journals Research on an Improved Sliding Mode Sensorless Six-Phase PMSM Control Strategy Based on ESO

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1292
Author(s):  
Hanying Gao ◽  
Guoqiang Zhang ◽  
Wenxue Wang ◽  
Xuechen Liu
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Sliding Mode ◽  
High Reliability ◽  
Permanent Magnet Synchronous Motor ◽  
Torque Ripple ◽  
Estimation Accuracy ◽  
Switching Function ◽  
Motor Speed ◽  
Rotor Position

The six-phase motor control system has low torque ripple, low harmonic content, and high reliability; therefore, it is suitable for electric vehicles, aerospace, and other applications requiring high power output and reliability. This study presents a superior sensorless control system for a six-phase permanent magnet synchronous motor (PMSM). The mathematical model of a PMSM in a stationary coordinate system is presented. The information of motor speed and position is obtained by using a sliding mode observer (SMO). As torque ripple and harmonic components affect the back electromotive force (BEMF) estimated value through the traditional SMO, the function of the frequency-variable tracker of the stator current (FVTSC) is used instead of the traditional switching function. By improving the SMO method, the BEMF is estimated independently, and its precision is maintained under startup or variable-speed states. In order to improve the estimation accuracy and resistance ability of the observer, the rotor position error was taken as the disturbance term, and the third-order extended state observer (ESO) was constructed to estimate the rotational speed and rotor position through the motor mechanical motion equation. Finally, the effectiveness of the method is verified by simulation and experiment results. The proposed control strategy can effectively improve the dynamic and static performance of PMSM.

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