不确定互联电力系统的RBF网络自适应混沌控制

Adaptive RBF Neural Network Control of Chaotic Interconnected Power System with Uncertainty

针对互联电力系统的混沌控制问题,建立了一种带有周期性负荷扰动和电磁功率扰动的互联电力系统模型,利用分岔图分析了该模型对各系统参数和外部干扰的敏感性.针对系统内部参数和扰动幅值的不确定性,提出了一种基于径向基函数神经网络的自适应滑模控制方法,实现了参数辨识,使系统输出能渐近跟踪目标轨迹,进而抑制了系统混沌.研究表明,该方法控制时间短、逼近误差小,而且有效地消除了抖振,具有实时控制、鲁棒性高等特点.

Chaotic control of the interconnected power system under the disturbances of periodic load and electromagnetic power was studied, and the sensitivity to internal time-variable dynamics and external disturbances through bifurcation diagrams was discussed. Considering the unknown system parameters, such as the sliding mode variable structure control and RBF neural network, we design the adaptive con- troller, which can realize parameters identification and stabilize chaos to the target orbit. Simulation results show that this method can not only eliminate the chattering characteristic, but reduce the control time and approximation error. It is of real-time control and has strong robustness, which is available to control chaos in other complex uncertain nonlinear systems.