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Neural Network Decoupling Control of Induction Motor Based on Inverse System

Author: ZhengYu
Tutor: YangWeiGuo
School: Northeastern University
Course: Control Theory and Control Engineering
Keywords: neural network inverse system induction motor speed rotor flux decoupling control
CLC: TM346
Type: Master's thesis
Year: 2008
Downloads: 77
Quote: 2
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Abstract


Three-phase alternating current induction motor is widely used in industry, because of its reliability, ruggedization, and the cost of it is relatively low. It’s a multivariable, strongly coupled, nonlinear system. When some parameters change, the conventional control method can’t adjust the control parameter in time during the motor running. It can’t satisfy the high requirement of speed regulation. So to design the induction motor control system with high precision and strong adaptability is more and more urgent.First, analyze the reversibility of the mathematical model of induction motor speed regulating system, with the inverse system method. On the basis of theoretic analysis, fabricate the a-th order neural network inverse system of the original system with the static neural network of BP and integrators constructed, and optimize the weight and the threshold of neural network with contractive mapping genetic algorithms. Then the methods, the steps, the design principles and the cautions of how to construct the a-th order neural network inverse system are given. Cascading the neural network inverse system with the original system, the system is decoupled into two independent pseudo-linear subsystems, speed subsystem and rotor flux subsystem. Then a linear close-loop adjustor is designed to control each of the decoupled subsystems.The simulation is done in MATLAB. Compared the two groups of experiments of induction motor in rated parameters and in load variation, this design method has preferably implemented the dynamic decoupling between the speed and rotor flux, with the method of the neural network a-th order inverse system. The system has preferable inhibit function against the disturbance of the load. The dynamic and static attributions have been improved obviously. The results of the experiment have approved that, the method of the neural network a-th order inverse system has preferable applied foreground.

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CLC: > Industrial Technology > Electrotechnical > Motor > AC motor > Induction motor
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