Decoupling Control for Bearingless Synchronous Reluctance Motor Based on Fuzzy Inverse Model

2014 ◽  
Vol 529 ◽  
pp. 524-528
Author(s):  
Zhang Li ◽  
Huang Qiu Zhu ◽  
Run Zhang Zeng
Keyword(s):  
Motor Control ◽  
Control System ◽  
Control Method ◽  
Dynamic Performance ◽  
Inverse Model ◽  
Decoupling Control ◽  
Dynamic Decoupling ◽  
Synchronous Reluctance Motor ◽  
Reluctance Motor ◽  
Motor Control System

For the bearingless synchronous reluctance motor (BSRM) is a multivariable, strong coupling, multi-input and multi-output system, based on the adaptive inverse control theory, a decoupling control method based on the T-S fuzzy inverse model identification is put forward in this paper. According to the input and output information of the system, a fuzzy inverse model of the motor control system is established, then making the inverse model and the original control system in series forms pseudo linear hybrid system to realize the approximate linearization and dynamic decoupling of the motor control system. Building the composite system and proceeding research in the Matlab/Simulink environment, the simulation results show that the control strategy can realize dynamic decoupling among the electromagnetic torque subsystem and the radial suspension force subsystem and among thex- andy-direction of the suspension force, and with excellent static and dynamic performance and adaptive ability.

Decoupling Control for Bearingless Synchronous Reluctance Motor Based on Neural Networks Inverse

2012 ◽  
Vol 150 ◽  
pp. 30-35
Author(s):  
Ze Bin Yang ◽  
Huang Qiu Zhu ◽  
Xiao Dong Sun ◽  
Tao Zhang
Keyword(s):  
Neural Networks ◽  
Control Method ◽  
Decoupling Control ◽  
Performance Simulation ◽  
Inverse Control ◽  
Linear Control Theory ◽  
Synchronous Reluctance Motor ◽  
Reluctance Motor ◽  
Static Performance ◽  
Simulation And Experiment

A novel decoupling control method based on neural networks inverse system is presented in this paper for a bearingless synchronous reluctance motor (BSRM) possessing the characteristics of multi-input-multi-output, nonlinearity, and strong coupling. The dynamic mathematical models are built, which are verified to be invertible. A controller based on neural network inverse is designed, which decouples the original nonlinear system to two linear position subsystems and an angular velocity subsystem. Furthermore, the linear control theory is applied to closed-loop synthesis to meet the desired performance. Simulation and experiment results show that the presented neural networks inverse control strategy can realize the dynamic decoupling of BSRM, and that the control system has fine dynamic and static performance.

Simulation and Design of Double Close-Loop DC Motor Control System Based on Matlab

2012 ◽  
Vol 546-547 ◽  
pp. 248-253
Author(s):  
Jing Jing Xiong ◽  
Zhen Feng ◽  
Jiao Yu Liu
Keyword(s):  
Motor Control ◽  
Control System ◽  
System Design ◽  
Closed Loop ◽  
Dynamic Performance ◽  
Model System ◽  
Optimal Parameters ◽  
Actual System ◽  
Dc Motor Control ◽  
Motor Control System

In this paper, based on the principle of the speed and current double closed loop DC regulating system and the requirement of the static and dynamic performance, we calculate time constant of the regulator, select the structure of the regulator to calculate related parameter and then correct its parameters, and model system and simulation by using Simulink, analysis waveform and debug to find out the optimal parameters of the system regulator to guide the actual system design.

Single Neuron PID Adaptive Control for BLDCM Based on Algebraic Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 851-854
Author(s):  
Yan Diao ◽  
Hong Ping Jia ◽  
Tian Jun Geng
Keyword(s):  
Adaptive Control ◽  
Motor Control ◽  
Control System ◽  
Pid Control ◽  
Control Method ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Brushless Dc ◽  
Dc Motor Control ◽  
Motor Control System

The brushless DC motor control system often adopts the classic PID control, the advantages of which are as follows: simple to control, easy to adjust the parameter and a certain degree of control precision. But it relies on accurate mathematical model. The permanent magnet brushless DC motor control system is a multi-variable and nonlinear. As to the deficiencies of the classic PID control method, this thesis proposes a method called artificial neural network PID adaptive control method, which is based on algebraic algorithm and overcomes the shortcomings such as the slow convergence of BP algorithm, easy to trap in local minimum, and etc.

Motor Control System Based on DSP Four-Bridge Arm Topology

2014 ◽  
Vol 644-650 ◽  
pp. 553-558
Author(s):  
Juan Feng
Keyword(s):  
Motor Control ◽  
Control System ◽  
Control Method ◽  
Dimensional Space ◽  
Asynchronous Motor ◽  
Speed Control ◽  
Torque Ripple ◽  
Space Vector ◽  
Speed Control System ◽  
Motor Control System

This paper proposes a space vector motor control system based on DSP four-legged bridge topology A three-dimensional space vector control principle based on four-bridge arm three-phase inverter is introduced with a MATLAB model using space vector Motor Control Algorithm instead of traditional Motor Control method. A DSP experimental setup is also carried out to verify the control theory and simulation result. The experiment and simulation results show that the speed control system helps the asynchronous motor to achieve the consistent response characteristics with separately excited DC machine. When the motor doesn't work or the load is unstable, asynchronous motor can still achieve excellent electrical characteristics with the proposed speed control system, such as quick start, small overshoot and static torque ripple.

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