Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system  

作　　者：YUAN Yuqi ZHOU Di LI Junlong LOU Chaofei 

机构地区：[1]School of Astronautics,Harbin Institute of Technology,Harbin 150001,China [2]Beijing Institute of Electronic System Engineering,Beijing 100854,China

出　　处：《Journal of Systems Engineering and Electronics》2024年第2期451-462,共12页系统工程与电子技术（英文版）

摘　　要：In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

关 键 词：long-short-term memory(LSTM)neural network extended Kalman filter(EKF) rolling training time-varying parameters estimation missile dual control system 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TP183[自动化与计算机技术—控制科学与工程] TJ765[兵器科学与技术—武器系统与运用工程]