基于神经网络的三相Vienna整流器全局快速终端滑模控制  

A Global Fast Terminal Sliding Mode Control for Three-Phase Vienna Rectifier Based on Neural Network

作　　者：李颖晖[1] 王瑶东 邱枭楠 郭旭 黄舜 LI Yinghui;WANG Yaodong;QIU Xiaonan;GUO Xu;HUANG Shun(Aviation Engineering School,Air Force Engineering University,Xi’an 710038,China;Unit 95133,Wuhan 430415,China)

机构地区：[1]空军工程大学航空工程学院,西安710038 [2]95133部队,武汉430415

出　　处：《空军工程大学学报》2022年第6期71-78,共8页Journal of Air Force Engineering University

基　　金：空军工程大学研究生创新实践基金。

摘　　要：针对Vienna整流器采用传统PI控制输出电压超调大,功率因数低,当系统参数发生变化时难以收敛,对交流侧输入电流干扰大等问题,提出一种基于神经网络的全局快速终端滑模控制策略。针对系统参数在实际环境中发生摄动和受到外界扰动,重新建立系统的不确定模型,将不确定项合并为总扰动并利用所建立自适应神经网络对其进行估计,并运用Lyapunov定理证明该非线性控制系统在系统参数摄动及外界扰动中可实现有界稳定。仿真结果表明:利用该方法提高了Vienna整流器功率因数,有效优化了输出电压超调高的问题并且有效降低了系统的谐波污染。最后搭建了实物样机,实验结果验证了上述结论的正确性。采用文中方法电压未出现超调,并且稳态响应时间减少了69%,切换负载电压波动减少了87%,动态响应时间减少了84%,谐波含量减少了68%。Aimed at the problems that the output voltage overshot is high,the power factor is low,the convergence is difficult while the system parameters change,and interference with ac input current of Vienna rectifier by using traditional PI control is heavy,a global fast terminal sliding mode control strategy based on neural network is proposed.In view of the perturbation of the system parameters and the external disturbances in the actual environment,an uncertainty model of the system is reconstructed,the uncertainties are combined with the total disturbance,and the adaptive neural network is utilized for estimating it,and the Lyapunov theorem is used to prove that the nonlinear control system can achieve bounded stability under the perturbation of the system parameters and the external disturbances.The simulation results show that the proposed method can improve the power factor of Vienna rectifier,overcome the problem of output voltage overshot and effectively reduce the harmonic pollution of the system.Finally,a physical prototype is built,and the experimental results show that the above conclusions are correct.The method presented in this paper has no overshoot,and the steady-state response time,switching load voltage fluctuation,dynamic response time and harmonic content are reduced by 69%,87%,84% and 68% respectively.

关 键 词：VIENNA整流器 自适应滑模控制 人工神经网络 不确定模型 非线性系统控制 

分 类 号：TM461[电气工程—电器]