摘要
针对无轴承永磁同步电机径向悬浮力之间存在的非线性、强耦合问题,提出基于神经网络逆系统方法的无轴承永磁同步电机径向悬浮力动态解耦控制策略。在分析无轴承永磁同步电机的工作原理和径向悬浮力产生机理的基础上,建立径向悬浮力的数学模型,并对该数学模型进行可逆性分析,证明此系统可逆。利用神经网络逆系统方法,将原来的非线性强耦合的多变量系统动态解耦成两个位置彼此无耦合的线性子系统,运用线性系统理论对线性子系统进行综合,设计系统的闭环控制器。在Matlab软件环境下构建仿真系统,并进行仿真研究和性能分析,仿真结果验证了该解耦策略的有效性。
According to the radial suspending forces of bearingless permanent magnet-type synchronous motors（BPMSM） being strongly nonlinear and coupled,a dynamic decoupling control strategy for radial suspension forces of the BPMSM based on neural network inverse（NNI） system method has been proposed.The working principle and radial suspension forces generating principle of the BPMSM were introduced.Then the mathematical model describing the dynamic behavior of the radial force model was set up,and the reversibility of the model was proved.The nonlinear and strongly coupled system was dynamic decoupled into two linear position subsystems using NNI method.Furthermore,linear control system techniques were applied to these decoupled linear subsystems to synthesize desired response,and then the close controller was designed.Finally,the simulation system is set up with Matlab software,and the simulation research and performance analysis were carried out.The simulation results have shown that the proposed decoupling method is effective.
出处
《电机与控制学报》
EI
CSCD
北大核心
2011年第11期21-26,共6页
Electric Machines and Control
基金
国家863高技术研究发展计划项目(2007AA04Z213)
江苏省2010年研究生科研创新计划基金(CX10B_270Z)
江苏高校优势学科建设工程资助项目
关键词
无轴承永磁同步电机
径向悬浮力
神经网络
逆系统
动态解耦
bearingless permanent magnet-type synchronous motor
radial suspending forces
neural networks
inverse system
dynamic decoupling
