An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation

An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assim- ilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model （Burgers＇ equation model） is used to demonstrate the efficacy of the proposed approach.

Resilient Control for Networked Control Systems Subject to Cyber/Physical Attacks

In this paper, we investigate a resilient control strategy for networked control systems（NCSs） subject to zero dynamic attacks which are stealthy false-data injection attacks that are designed so that they cannot be detected based on control input and measurement data. Cyber resilience represents the ability of systems or network architectures to continue providing their intended behavior during attack and recovery. When a cyber attack on the control signal of a networked control system is computed to remain undetectable from passive model-based fault detection and isolation schemes, we show that the consequence of a zero dynamic attack on the state variable of the plant is undetectable during attack but it becomes apparent after the end of the attack. A resilient linear quadratic Gaussian controller, having the ability to quickly recover the nominal behavior of the closed-loop system after the attack end, is designed by updating online the Kalman filter from information given by an active version of the generalized likelihood ratio detector.

Lateral Flight Technical Error Estimation Model for Performance Based Navigation

Flight technical error （FTE） combined with navigation system error （NSE） is the main part of total system error （TSE） in performance based navigation （PBN）. The implementation of PBN requires pre-flight prediction and en-route short-term dynamical prediction of the TSE. Once the sum of predicted lateral FTE and NSE is greater than the specified PBN value, the PBN cannot operate. Thus, accurate modeling and thorough analysis of lateral FTE are indispensible. Multiple-input multiple-output （MIMO） lateral track control system of a transport aircraft is designed using linear quadratic Gaussian and loop transfer recovery （LQG/LTR） method, and the lateral FTE of a turbulence disturbed approach operation is analyzed. The error estimation mapping function of latera FTE and its bound estimation algorithm are proposed based on singular value theory. According to the forming mechanism of lateral FTE, the algorithm considers environmental turbulence fluctuation disturbance, aircraft dynamics and con- trol system parameters. Real-data-based Monte-Carlo simulation validates the theoretical analysis of FTE. It also shows that FTE is mainly caused by turbulence fluctuation disturbance when automatic flight control system （AFCS） is engaged and would in- crease with escalating environmental turbulence intensity.