Kalman-filter control schemes for fringe tracking. Development and application to VLTI/GRAVITY

The implementation of fringe tracking for optical interferometers is inevitable when optimal exploitation of the instrumental capacities is desired. Fringe tracking allows continuous fringe observation, considerably increasing the sensitivity of the interferometric system. In addition to the correction of atmospheric path-length differences, a decent control algorithm should correct for disturbances introduced by instrumental vibrations, and deal with other errors propagating in the optical trains. We attempt to construct control schemes based on Kalman filters. Kalman filtering is an optimal data processing algorithm for tracking and correcting a system on which observations are performed. As a direct application, control schemes are designed for GRAVITY, a future four-telescope near-infrared beam combiner for the Very Large Telescope Interferometer (VLTI). We base our study on recent work in adaptive-optics control. The technique is to describe perturbations of fringe phases in terms of an a priori model. The model allows us to optimize the tracking of fringes, in that it is adapted to the prevailing perturbations. Since the model is of a parametric nature, a parameter identification needs to be included. Different possibilities exist to generalize to the four-telescope fringe tracking that is useful for GRAVITY. On the basis of a two-telescope Kalman-filtering control algorithm, a set of two properly working control algorithms for four-telescope fringe tracking is constructed. The control schemes are designed to take into account flux problems and low-signal baselines. First simulations of the fringe-tracking process indicate that the defined schemes meet the requirements for GRAVITY and allow us to distinguish in performance. In a future paper, we will compare the performances of classical fringe tracking to our Kalman-filter control.Comment: 17 pages, 8 figures, accepted for publication in A&

Smart Power Grid Synchronization With Fault Tolerant Nonlinear Estimation

Effective real-time state estimation is essential for smart grid synchronization, as electricity demand continues to grow, and renewable energy resources increase their penetration into the grid. In order to provide a more reliable state estimation technique to address the problem of bad data in the PMU-based power synchronization, this paper presents a novel nonlinear estimation framework to dynamically track frequency, voltage magnitudes and phase angles. Instead of directly analyzing in abc coordinate frame, symmetrical component transformation is employed to separate the positive, negative, and zero sequence networks. Then, Clarke\u27s transformation is used to transform the sequence networks into the αβ stationary coordinate frame, which leads to system model formulation. A novel fault tolerant extended Kalman filter based real-time estimation framework is proposed for smart grid synchronization with noisy bad data measurements. Computer simulation studies have demonstrated that the proposed fault tolerant extended Kalman filter (FTEKF) provides more accurate voltage synchronization results than the extended Kalman filter (EKF). The proposed approach has been implemented with dSPACE DS1103 and National Instruments CompactRIO hardware platforms. Computer simulation and hardware instrumentation results have shown the potential applications of FTEKF in smart grid synchronization

Dynamic state reconciliation and model-based fault detection for chemical processes

In this paper, we present a method for the fault detection based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurements using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical processe

F-8C adaptive flight control extensions

An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated

Predictive Shutdown Systems for Nuclear Power Plants

This dissertation investigates the use of a Kalman filter (KF) to predict, within the shutdown system (SDS) of a nuclear power plant (NPP), whether a safety parameter measurement will reach a corresponding trip set-point (TSP). The proposed predictive SDS (PSDS) designs aim to initiate shutdown actions at a time which is earlier than conventional shutdown initiation. These early detections are, in turn, expected to improve safety and productivity margins within the NPP. The KF-based point-PSDS design utilizes a linear time-varying (LTV) system model to predict mean safety parameter measurements for comparison against the TSP. The KF considers noise covariances that either have assumed predetermined values, or are estimated online using an adaptive limited memory filter (ALMF). The PSDS is enhanced to consider, by recursive least squares (RLS) estimation, conditions that are uncertain with respect to the assumed system model and noise properties. The result is a KF⁄RLS-based PSDS that compensates for prediction error by RLS-based estimation of deterministic disturbances to the system state and measurement. The PSDS is further enhanced to calculate confidence intervals for the predictions as a function of the propagated error covariance. This enhancement results in interval-PSDS designs that consider confidence in an impending condition by comparing predetermined confidence interval bounds against the TSP. Finally, an optimal-PSDS design is formulated to adapt the effective prediction, e.g. horizon or bias, by limiting and minimizing the probability of missed and false trip occurrences respectively using hypothesis testing methods and optimal alarm theory. In this manner, the optimal-PSDS is made aware of the quality of past predictions. The PSDS designs are compared, through simulation and experiment, against a conventional SDS in terms of response time or time-to-trip for the steam generator level low (SGLL) safety parameter under various conditions of uncertainty, e.g. parameter error or unmeasurable signals. MATLAB-based simulations demonstrate that the PSDS designs are able to reduce time-to-trip. The PSDS designs are then implemented within a Tricon v9 safety-PLC with a scan time that adheres to current nuclear industry regulations. The experimental results reveal that a reduced time-to-trip can be achieved for a real-world system with unknown system-model mismatch

Comparison of fringe-tracking algorithms for single-mode near-infrared long-baseline interferometers

To enable optical long baseline interferometry toward faint objects, long integrations are necessary despite atmospheric turbulence. Fringe trackers are needed to stabilize the fringes and thus increase the fringe visibility and phase signal-to-noise ratio (SNR), with efficient controllers robust to instrumental vibrations, and to subsequent path fluctuations and flux drop-outs. We report on simulations, analysis and comparison of the performances of a classical integrator controller and of a Kalman controller, both optimized to track fringes under realistic observing conditions for different source magnitudes, disturbance conditions, and sampling frequencies. The key parameters of our simulations (instrument photometric performance, detection noise, turbulence and vibrations statistics) are based on typical observing conditions at the Very Large Telescope observatory and on the design of the GRAVITY instrument, a 4-telescope single-mode long baseline interferometer in the near-infrared, next in line to be installed at VLT Interferometer. We find that both controller performances follow a two-regime law with the star magnitude, a constant disturbance limited regime, and a diverging detector and photon noise limited regime. Moreover, we find that the Kalman controller is optimal in the high and medium SNR regime due to its predictive commands based on an accurate disturbance model. In the low SNR regime, the model is not accurate enough to be more robust than an integrator controller. Identifying the disturbances from high SNR measurements improves the Kalman performances in case of strong optical path difference disturbances.Comment: Accepted for publication in A&A. 17 pages 15 figure

Прогнозирование индекса потребительских цен в Украине с использованием регрессионных моделей и фильтра Калмана

Роботу присвячено розв’язанню задачі короткострокового прогнозування індексу споживчих цін в Україні на основі регресійних моделей і адаптивного фільтра Калмана. Побудовано адекватну модель для прогнозування індексу споживчих цін і застосовано адаптивний фільтр Калмана для отримання оптимальних оцінок стану досліджуваного процесу і обчислення короткострокового прогнозу. Основні результати роботи: реалізація і застосування двох модифікацій фільтра Калмана (звичайний та адаптивний), орієнтовані на оцінювання коваріацій випадкових збурень стану та похибок вимірів. Альтернативні регресійні моделі та оцінки короткострокових прогнозів, отримані на основі фільтра. Надано порівняльний аналіз отриманих результатів. Для аналізу використано статистичну інформацію перехідної економіки України.The paper considers the problem of short term forecasting of consumer price index using regression models and adaptive Kalman filter. The main purpose of the study is constructing of high quality model for forecasting of consumer price index and application of Kalman filter for computing optimal estimates of states for the process under investigation. The basic results of the study are as follows: two modifications of the Kalman filter (ordinary and adaptive), directed towards estimation of covariances for stochastic state disturbances and measurement errors. Alternative short term forecasts are generated with regression models and Kalman filters. A comparative analysis of results achieved is given. The necessary statistical data was taken from Ukrainian economy in transition.Работа посвящена решению задачи краткосрочного прогнозирования индекса потребительских цен в Украине на основе регрессионных моделей и адаптивного фильтра Калмана. Построена адекватная модель для прогнозирования индекса потребительских цен и использован адаптивный фильтр Калмана для получения оптимальных оценок состояний исследуемого процесса и краткосрочных прогнозов. Основные результаты работы: две модификации фильтра Калмана (обычный и адаптивный), ориентированные на оценивание ковариации случайных возмущений состояния и погрешностей измерений. Альтернативные регрессионные модели и оценки краткосрочных прогнозов получены при помощи фильтра. Дан сравнительный анализ достигнутых результатов. Для анализа использована статистическая информация переходной экономики Украины

An intelligent navigation system for an unmanned surface vehicle

Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable opringei to undertake various environmental monitoring tasks. Synergistically, sensor mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to enhance the robustness and fault tolerance of the onboard navigation system. This thesis not only provides a holistic framework but also a concourse of computational techniques in the design of a fault tolerant navigation system. One of the principle novelties of this research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading angle under various fault situations for Springer. This algorithm adapts the process noise covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach to enhance the fault tolerance of the heading angles for Springer. To the author's knowledge, the work presented in this thesis suggests a novel way forward in the development of autonomous navigation system design and, therefore, it is considered that the work constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD AND SOUTH WEST WATER PL