摘要
针对具有参数不确定性和逆变器非线性的永磁同步电机驱动系统,提出了一种基于小波神经网络-自适应反步无模型控制的永磁同步电机速度跟踪控制方案。首先建立了永磁同步电机系统的新型超局部模型,构建的超局部模型能够在线估计永磁同步电机系统的非参数模型,包括参数不确定性、逆变器非线性和负载扰动;然后基于超局部模型设计了小波神经网络-自适应反步无模型速度控制器;最后通过仿真验证了该方案的有效性,结果表明所提出的无模型控制永磁同步电机驱动系统与传统的反推控制永磁同步电机驱动系统相比,不仅可以估计并消除包括系统未建模部分和未知干扰在内的各种不确定性,而且稳态性能更好、动态响应更快。
Aiming at the permanent magnet synchronous motor(PMSM)drive system with parameter uncertainty and inverter nonlinearity,a wavelet neural network based on model-free adaptive backstepping control for PMSM speed tracking control is proposed.First,the new hyperlocal model of the permanent magnet synchronous motor system is established,the constructed hyperlocal model can accurately identify the nonparametric model of the permanent magnet synchronous motor system,including parameter uncertainty,inverter nonlinearity and load disturbance.Then,an adaptive backstepping model-free speed controller is designed based on hyperlocal model is designed.Finally,the effectiveness of the proposed scheme is verified by simulation,the results show that the proposed model-free control PMSM drive system,compared with the traditional backstepping control PMSM drive system,can not only estimate and eliminate various uncertainties including the unmodeled part of the system and unknown disturbances,but also has better steady-state performance and faster dynamic response.
作者
郝娜
詹志坤
HAO Na;ZHAN Zhikun(Electrical and electronic engineering department,Shijiazhuang University of Applied Technology,Shijiazhuang,Hebei 050081,China;Key Lab of Industrial Computer Control Engineering of Hebei Province,Yanshan University.Qinhuangdao,Hebei 066004,China)
出处
《燕山大学学报》
CAS
北大核心
2022年第3期239-245,256,共8页
Journal of Yanshan University
基金
河北省自然科学基金资助项目(F2021203070)。
关键词
反步控制
无模型
小波神经网络
backstepping control
model-free
wavelet neural network
