摘要
基于迭代学习控制理论提出了一种可变学习增益的迭代学习律,在非线性系统中对期望轨迹进行跟踪,与学习增益不变的迭代学习控制相比较,收敛速度得到很大的提高;通过对其收敛性进行严格的数学证明,得到了迭代学习律收敛的充分条件;在单机无穷大系统中,将该控制律应用于同步发电机的励磁控制,仿真结果表明该控制律的有效性,改善了控制的动态特性,有利于提高电力系统稳定性.
An iterative learning law with variable gain is proposed based on iterative learning control theory. The convergence is strictly proved mathematically and sufficient conditions are obtained. The control law is then applied to the excitation control of synchronous machines in single machine to infinite system. Simulations are also performed by in the single machine to infinite system to demonstrate the validity and universality of the method.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2007年第5期856-860,共5页
Control Theory & Applications
基金
航空基金资助项目(04F53036)
关键词
迭代学习控制
单机–无穷大系统
收敛性
同步发电机
励磁控制
iterative learning control
a single machine to infinite system
convergence
synchronous machine
excitation control
