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Online learning RBF neural network-based adaptive compensative control scheme for governor system
Pages: 13-19
Year: Issue:  2
Journal: Electric Machines and Control

Keyword:  governor systemtransient stabilityRBF neural networkonline learningcompensative controlinverse system;
Abstract: 汽门控制对于提高电力系统暂态稳定具有重要作用.为了提高汽门系统的控制性能,提出了基于在线学习RBF神经网络的汽门开度自适应补偿控制方法.首先,根据逆系统方法分析了被控汽门系统的可逆性、推导了被控汽门系统输出的α阶导数和伪控制量之间的误差,并设计了用于补偿此误差的在线学习RBF神经网络.然后,基于Lyapunov稳定性理论设计了RBF神经网络的在线学习算法,证明了闭环系统跟踪误差和RBF神经网络权值...
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