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(LST...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.展开更多
Accurate control of slab temperature and heating rate is an important significance to improve product performance and reduce carbon emissions for steel rolling reheating furnace(SRRF).Firstly,a spatial temporal distri...Accurate control of slab temperature and heating rate is an important significance to improve product performance and reduce carbon emissions for steel rolling reheating furnace(SRRF).Firstly,a spatial temporal distributed-nonlinear autoregressive with exogenous inputs correlation model(STD-NARXCM)to spatial temporal distributed-autoregressive with exogenous inputs correlation model(STD-ARXCM)in working point is established.Secondly,a new coordinated time-sharing control architecture in different time periods is proposed,which is along the length of the SRRF to improve the control performance.Thirdly,a hybrid control algorithm of expert-fuzzy is proposed to improve the dynamic of the temperature and the heating rate during time period 0 to t_(1).A hybrid control algorithm of expert-fuzzy-PID is proposed to enhance the control accuracy and the heating rate during time period t_(1) to t_(2).A hybrid control algorithm of expert-active disturbance rejection control(ADRC)is proposed to boost the anti-interference and the heating rate during time period t_(2) to t_(3).Finally,the experimental results show that the coordinated time-sharing algorithm can meet the process requirements,the maximum deviation of temperature value is 8-13.5℃.展开更多
文摘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.
基金This work was supported by the National Natural Science Foundation of China(Nos.62173032 and 62003038).
文摘Accurate control of slab temperature and heating rate is an important significance to improve product performance and reduce carbon emissions for steel rolling reheating furnace(SRRF).Firstly,a spatial temporal distributed-nonlinear autoregressive with exogenous inputs correlation model(STD-NARXCM)to spatial temporal distributed-autoregressive with exogenous inputs correlation model(STD-ARXCM)in working point is established.Secondly,a new coordinated time-sharing control architecture in different time periods is proposed,which is along the length of the SRRF to improve the control performance.Thirdly,a hybrid control algorithm of expert-fuzzy is proposed to improve the dynamic of the temperature and the heating rate during time period 0 to t_(1).A hybrid control algorithm of expert-fuzzy-PID is proposed to enhance the control accuracy and the heating rate during time period t_(1) to t_(2).A hybrid control algorithm of expert-active disturbance rejection control(ADRC)is proposed to boost the anti-interference and the heating rate during time period t_(2) to t_(3).Finally,the experimental results show that the coordinated time-sharing algorithm can meet the process requirements,the maximum deviation of temperature value is 8-13.5℃.
