Effective model based fault detection scheme for rudder servo system

The inherent nonlinearities of the rudder servo system(RSS) and the unknown external disturbances bring great challenges to the practical application of fault detection technology. Modeling of whole rudder system is a challenging and difficult task. Quite often, models are too inaccurate, especially in transient stages. In model based fault detection, these inaccuracies might cause wrong actions. An effective approach, which combines nonlinear unknown input observer(NUIO) with an adaptive threshold, is proposed. NUIO can estimate the states of RSS asymptotically without any knowledge of external disturbance. An adaptive threshold is used for decision making which helps to reduce the influence of model uncertainty. Actuator and sensor faults that occur in RSS are considered both by simulation and experimental tests. The observer performance, robustness and fault detection capability are verified. Simulation and experimental results show that the proposed fault detection scheme is efficient and can be used for on-line fault detection.

Robust Fault-Tolerant Control for Longitudinal Dynamics of Aircraft with Input Saturation

A robust fault-tolerant control scheme is proposed for the longitudinal dynamics of an aircraft with input saturation,using the anti-windup method and the fault detection observer technology.To estimate the system fault,a detection observer is designed for the longitudinal dynamics,and a fault-tolerant control law is developed to compensate for the fault effects of the longitudinal dynamics.Then,an anti-windup compensator is augmented into the fault-tolerant control law to eliminate the effect of input saturation.Using linear matrix inequality(LMI)technology,the detection observer based fault-tolerant controller is designed to ensure the stability of the closed-loop system and the convergence of the detection observer.Finally,the developed robust fault-tolerant control scheme is applied to the longitudinal model of an aircraft and simulation results are presented to illustrate the effectiveness of the proposed control scheme.

How to co-add images? I. A new iterative method for image reconstruction of dithered observations

By employing the previous Voronoi approach and replacing its nearest neighbor approx- imation with Drizzle in iterative signal extraction, we develop a fast iterative Drizzle algorithm, namedfiDrizzle, to reconstruct the underlying band-limited image from undersampled dithered frames. Compared with the existing iDrizzle, the new algorithm improves rate of convergence and accelerates the computational speed. Moreover, under the same conditions （e.g. the same number of dithers and iterations）, fiDrizzle can make a better quality reconstruction than iDrizzle, due to the newly discov- ered High Sampling caused Decelerating Convergence （HSDC） effect in the iterative signal extraction process.fiDrizzle demonstrates its powerful ability to perform image deconvolution from undersampled dithers.

Improved Flight Conflict Detection Algorithm Based on Gauss-Hermite Particle Filter

In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.