光电稳定平台神经网络自抗扰控制方法

摘要

提出了一种采用BP神经网络对自抗扰控制器中的非线性扩张状态观测器进行参数整定的方法,简化了非线性扩张状态观测器繁琐的参数整定过程,并对这种结合了BP神经网络的自抗扰控制系统进行了仿真分析.仿真结果表明,加入BP神经网络的自抗扰控制系统可以显著地提高机载光电稳定平台的扰动隔离度,控制效果明显比传统自抗扰控制方法好,对提高机载光电稳定平台的视轴稳定精度具有重要意义.%A parameter setting method is proposed for the nonlinear extended state observer in the Active Disturbance-Rejection Controller (ADRC) by using Back-Propagation Neural Network (BPNN).This method simplifies the complicated parameter setting process of the nonlinear extended state observer.Simulation is made to the BPNN based ADRC system.The simulation results show that this method can significantly enhance the disturbance isolation of the airborne optoelectronic stabilized platform,and the control effect is obviously superior to that of the traditional active disturbance-rejection control method,which is of great significance for improving the optical-axis stabilizing accuracy of the airborne optoelectronic stabilized platform.