首页|Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system
Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system
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维普
In this paper,a filtering method is presented to esti-mate 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 net-work 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 suffi-ciently small,the system is asymptotically stable.With its appli-cation to the estimation of time-varying parameters of a missile dual control system,the LSTM-EKF shows better filtering perfor-mance than the EKF and adaptive EKF(AEKF)when there exist large uncertainties in the system model.
long-short-term memory(LSTM)neural networkextended Kalman filter(EKF)rolling trainingtime-varying parameters estimationmissile dual control system
YUAN Yuqi、ZHOU Di、LI Junlong、LOU Chaofei
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School of Astronautics,Harbin Institute of Technology,Harbin 150001,China
Beijing Institute of Electronic System Engineering,Beijing 100854,China
NETL
NSTL
万方数据
维普
