基于神经元解耦RBF网络多电机系统参数PID控制  

RBF Neural Network of Multi-motor System PID Control Based on Neuron Decoupling Technology

作　　者：赵亮[1] 刘星桥[1] 陈冲[1] 白雪飞[1] 

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《电气传动》2009年第1期59-62,共4页Electric Drive

基　　金：江苏省自然科学基金项目(BK2003049)

摘　　要：以多变量非线性强耦合的两电机同步控制系统为研究对象,针对电流跟踪型SPWM变频器供电的感应电机系统,在α,β静止坐标系下,建立两台电机系统的数学模型。采用RBF网络自适应PID控制器和自适应神经元解耦补偿器的解耦控制技术,对两电机同步系统的速度和张力进行解耦控制。实验结果表明:采用该方法设计的控制器可以实现两电机同步系统中速度和张力的解耦控制,系统具有良好的动静态性能和跟随性能。Taking the multi-variable of synchronization system of the AC induction motors as study object, focusing on the system of induction motors powered by current-tracte SPWM transducers, the unified mathematical model of the system of two motors depends on α,β static reference was established. RBF neural network adaptive PID controller and decoupling technology of adaptive neuron decoupling compensator was adopt- ed to perform the decoupling control of speed and tension of the system. The experiment results show that the two-motor synchronous system is decoupled based on the controller designed with better dynamic and static characteristics and following performance.

关 键 词：自适应PID 神经元 解耦控制 速度 张力 

分 类 号：TM92[电气工程—电力电子与电力传动]