Ch. Hajiyev

Tracy-Widom distribution based fault detection approach: application to aircraft sensor/actuator fault detection.

The fault detection approach based on the Tracy-Widom distribution is presented and applied to the aircraft flight control system. An operative method of testing the innovation covariance of the Kalman filter is proposed. The maximal eigenvalue of the random Wishart matrix is used as the monitoring statistic, and the testing problem is reduced to determine the asymptotics for the largest eigenvalue of the Wishart matrix. As a result, an algorithm for testing the innovation covariance based on the Tracy-Widom distribution is proposed. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model is considered, and detection of sensor and control surface faults in the flight control system which affect the innovation covariance, are examined.

Attitude determination and control system design of the ITU-UUBF LEO1 satellite

Abstract In this paper an attitude determination and control system for ITU-UUBF LEO1 satellite is proposed. To determine the attitude of the satellite this system uses algebraic method (2-vector algorithm). As a reference direction, the unit vectors toward the Sun and the Earths center, and the Earths magnetic field are used. Thus, it includes three different 2-vector algorithms based on using Earths magnetic field–Sun vector, Earths magnetic field–nadir vector, and nadir vector–Sun vector couples. A redundant data processing algorithm based on maximum likelihood was designed. The parameters of satellites rotational motion are estimated using extended Kalman filter (EKF). For control purposes an EKF-based PD controller is designed. To reveal the performance of the designed system a simulation is made.

Kalman Filter Innovation Sequence Based Fault Detection in LEO Satellite Attitude Determination and Control System

In this paper, fault detection algorithm for LEO satellite attitude determination and control system using an approach for checking the statistical characteristics of Extended Kalman filter (EKF) innovation sequence is proposed. It is based on given statistics for the mathematical expectation of the spectral norm of the normalized innovation matrix of the EKF. The attitude dynamics of the LEO satellite as an example, is considered, and detection of various sensor faults affecting the mean and variance is examined.

Sensor fault detection by testing the largest eigenvalue of the innovation covariance using Tracy-Widom distribution

Operative method of testing the innovation covariance of the Kalman filter is proposed. The maximal eigenvalue of the random Wishart matrix is used in this process as monitoring statistic, and the testing problem is reduced to determine the asymptotics for largest eigenvalue of the Wishart matrix. As a result, algorithm for testing the innovation covariance based on Tracy-Widom distribution is proposed. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model is considered, and detection of pitch rate gyro, air speed indicator and angle of attack sensor failures, which affect the innovation covariance, are examined.

Stopping rules formation and faults detection in parametric identification problems

Abstract An approach to the generation of stopping rules in parametric identification problems is proposed on the basis of the computation of a statistic of the difference between two successive estimates. This statistic is also used for fault detection in the Kalman filter. The developed decision rules are applied to a linear system identification problem. Experimental results are presented to demonstrate the performance of the proposed algorithms.

In-flight calibration of pico satellite attitude sensors via Unscented Kalman filter

In this paper an Unscented Kalman filter based procedure is proposed for the bias estimation of attitude sensors of a pico satellite. Three axis magnetometers and the rate gyros are used as attitude sensors. At the initial phase, biases of three orthogonally located magnetometers are estimated as well as the attitude and attitude rates of the satellite. During initial period after the orbit injection, gyro measurements are accepted as bias free, since the precise gyros are working accurately and the accumulated gyro biases are negligible. At the second phase, estimated constant magnetometer bias components are taken into account and the algorithm is run for the estimation of the gyro biases that are cumulatively increased by time. As a result, six different bias terms for two different sensors are obtained in two stages where attitude and attitude rates are estimated regularly. For both estimation phases of the procedure Unscented Kalman Filter is used as the estimation algorithm.

An approach to improve the offshore platform coordinates accuracy by using multichannel Kalman filtering.

In this paper, multichannel Kalman filters for estimation of offshore platform (OP) coordinates are designed. The complete OP motion is assumed to be composed of the low-frequency motion caused by the wind and undercurrent, and the high-frequency motion caused by the sea. The mathematical model of the low-frequency OP motion is given by the normal differential equation system, and the high-frequency OP motion is represented by a moving-average multivariable autoregression model. The parameter estimation problem for the model of the low-frequency OP motion, on which the in-service control is performed, is solved through two jointly operating Kalman filters: the first one is for the estimation of the parameters of the low-frequency motion model, and the second one is for the parameter estimation for the high-frequency model. The parameters of the first filter are automatically adapted to variations of the second filter, i.e., they are adapted to disturbances from the sea. Two algorithms for the OP motion parameter estimation (parallel and with preliminary data compression). employed for several measuring channels data estimation, are developed, and simulated on a computer. Some recommendations on their use are given.

Integration of Air Data System and Doppler radar via Kalman filtering

In this study, Integrated Air Data – Doppler Navigation System is developed and presented. Air Data System and Doppler Radar have different advantages and disadvantages in applications in flight control and navigation systems. The reason of this integration is to achieve the best combination of the features and eliminate the disadvantages of these systems. As a consequence of this integration via Kalman filtering, an integrated navigation system with high accuracy of airvehicle velocity and position and with high measurement frequency is attained. On basis of this integrated system, the possibility to estimate accurate and real time inflight wind speed is explained.

Modification of A Low-Cost Gps Receiver and Improvement of Position Data

In this study a modified low-cost GPS receiver has been built. A linear Kalman filter for improvement of GPS position data when n ges 4 satellites are in view, has been implemented. A satellite number based self-adapted Kalman filter has been developed. The proposed low-cost GPS receiver and Kalman filter have been examined for real GPS data.

Self-Tuning Kalman Filter Design for Offshore Platform Coordinates Estimation

Abstract In this paper, a self-tuning Kalman filter for offshore platform (OP) position supervision is designed. The complete OP motion is assumed to be composed of the low-frequency motion caused by the wind and undercurrent, and the high frequency motion caused by the sea-way. The state estimation problem for the model of the low-frequency OP motion, on which the in-service control is performed, is solved through two jointly operating Kalman filters: the first one is used for the estimation of the states of the low-frequency motion, and the second one is employed for the highfrequency one. The parameters of the first filter are automatically adapted to variations of the second filter.