基于DBFNN的后推设计及其在电力系统励磁控制中的应用  

DBFNN Based BackStepping Design and Its Application in Power Systems

作　　者：时海涛[1] 安冬[1] 

机构地区：[1]中国科学院半导体研究所神经网络实验室,北京100083

出　　处：《电子学报》2004年第11期1766-1769,共4页Acta Electronica Sinica

基　　金：国家自然科学基金 (No .90 2 0 70 0 8)

摘　　要：本文采用后推设计算法为一类严格反馈系统设计了基于方向基函数神经网络 (DBFNN)的自适应控制器 .在后推算法中的每步都引入一积分型的Lyapunov函数来设计一个虚拟控制器 ,并在最后一步为闭环系统综合设计了神经网络控制器 .网络权值的调整基于所选择的Lyapunov函数 ,于是设计方案能保证整个闭环系统是最终一致有界的 .把所设计控制方案用于带有未知参数和外部干扰的电力系统励磁控制中 .For a class of strict feedback nonlinear system,a nonlinear adaptive controller was first investigated.The design procedure used backstepping method based on DBFNN (Direction Basis Neural Network).In each step of backstepping a virtual controller was designed by introducing a suitable Lyapunov function.In the last step the singular-free real controller was synthesized.The tuning law of NN weights was derived from the selected integral Lyapunov function.So the stability of the closed loop can be guaranteed. Then the proposed scheme was applied to design an excitation controller for one power system with unknown disturbance and unknown parameters.The simulation shows the validity of the proposed method.

关 键 词：非线性自适应 方向基神经网络 励磁控制 后推算法 最终一致有界 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]