摘要
无轴承永磁同步电机是一个强耦合的非线性复杂系统,实现无轴承永磁同步电机的线性化解耦控制,是无轴承永磁同步电机稳定运行和走向实用化的关键。将神经网络具有的特点(对非线性系统的逼近能力以及对系统参数变化的适应能力)与逆系统方法的特点(解耦线性化)相结合,提出了基于神经网络的无轴承永磁同步电机逆系统解耦控制方法。通过用静态神经网络加积分器来构造无轴承永磁同步电机的逆系统,将无轴承永磁同步电机动态解耦成位移子系统和转速子系统分别设计调节器进行控制,然后运用线性系统理论进行综合。仿真及实验结果表明,系统具有良好的鲁棒性和动静态解耦性能。
The bearingless permanent magnet- type synchronous motor is a strong- coupled complicated nonlinear system. The linearization and decoupling control is a key to stable operation and practicability for this motor. Based on artificial neural networks,an inverse system deeoupling control method for bearingless permanent magnet--type synchronous motors was proposed. This method combined approximation ability to nonlinear system, adaptability to parameter variations of artificial neural networks and decoupling characteristics of inverse system. Cascading the ANN inverse which consists of a static ANN and five integrators with the motor, the system was decoupled into two independent second--order linear subsystems and a first--order one, which were two displacement subsystems and a rotor speed one, so as to be easy to design the closed--loop linear regulator to control each of the subsystems. Simulation and experimental results show that strong robustness, good static and dynamic decoupling performance can be achieved by using the proposed method.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2008年第22期2681-2686,2693,共7页
China Mechanical Engineering
基金
天津市科技发展计划资助项目(05ZHGCGX00200)
关键词
无轴承永磁同步电机
神经网络
逆系统
解耦控制
径向悬浮力
bearingless permanent magnet-type synchronous motor
artificial neural networks
inverse system
decoupling control
radial levitation force
