基于RBF神经网络的电力系统自适应励磁控制器的设计

DESIGN OF ADAPTIVE EXCITATION CONTROLLER FOR A POWER SYSTEM BASED ON RADIAL BASIS NEURAL NETWORK

采用后推设计算法设计了SISO严格反馈系统的RBF神经网络自适应控制器。权值的调整算法基于所选择的积分型的Lyapunov函数 ,能保证整个闭环系统是最终一致有界的。把所设计的控制方案用于电力系统的励磁控制中。仿真结果表明 。

An adaptive controller based on radial basis neural network of SISO strict-feedback system was designed by using back-stepping method. The tuning laws for weights of neural network were derived from an integral Lyapunov function which was newly selected. So the stability of the closed loop can be guaranteed. The proposed scheme has been applied to design of an excitation controller for a power system. The simulation shows the good tracking performances and robustness of the newly designed controller.