基于在线学习的机载光电系统扩张状态观测器参数整定研究

Parameter Setting of Extended State Observer in Airborne Electro-Optical System Based on Online Learning

针对非线性动态系统的扩张状态观测器(ESO)参数整定问题,建立了基于BP神经网络的参数整定模型。采用在线梯度下降法进行网络训练以保证对动态系统的学习能力,并引入了IDBD算法,利用输入数据的信息和学习过程中的经验实现学习速率的自适应调整,以改进在线梯度下降法的适应性。数字仿真表明,该参数整定模型较传统的参数整定模型具有动态性能好、精度高等优点,能够提高非线性系统扩张状态观测器参数的动态整定精度,进而在一定程度上改善自抗扰控制器的系统控制性能。

In order to solve the problem of parameter setting of the extended state observer used in nonlinear dynamic system,a parameter setting model based on BP neural network is established.The online gradient descent method is used to train the network so as to ensure the learning ability of the dynamic system.The incremental Delta-Bar-Delta algorithm is introduced,and the information of input data and learning experience are used to realize the adaptive adjustment of the learning rate and improve the adaptability of the online gradient descent method.It can be seen from the numerical simulation that the parameter setting model possesses the advantages of good dynamic performance and high accuracy compared with the traditional parameter setting model,and it can improve the dynamic setting accuracy of the parameters of the extended state observer of the nonlinear system,so that the controlling performance of the ADRC system is improved to a certain extent.