7,232 research outputs found
A Tractable Fault Detection and Isolation Approach for Nonlinear Systems with Probabilistic Performance
This article presents a novel perspective along with a scalable methodology
to design a fault detection and isolation (FDI) filter for high dimensional
nonlinear systems. Previous approaches on FDI problems are either confined to
linear systems or they are only applicable to low dimensional dynamics with
specific structures. In contrast, shifting attention from the system dynamics
to the disturbance inputs, we propose a relaxed design perspective to train a
linear residual generator given some statistical information about the
disturbance patterns. That is, we propose an optimization-based approach to
robustify the filter with respect to finitely many signatures of the
nonlinearity. We then invoke recent results in randomized optimization to
provide theoretical guarantees for the performance of the proposed filer.
Finally, motivated by a cyber-physical attack emanating from the
vulnerabilities introduced by the interaction between IT infrastructure and
power system, we deploy the developed theoretical results to detect such an
intrusion before the functionality of the power system is disrupted
Modeling, Analysis, and Optimization Issues for Large Space Structures
Topics concerning the modeling, analysis, and optimization of large space structures are discussed including structure-control interaction, structural and structural dynamics modeling, thermal analysis, testing, and design
Sensitivity Study for UAV GPS-Denied Navigation in Uncertain Landmark Fields
This document provides two 2D simulation sensitivity analyses regarding a drone’s flight characteristic (state) errors within a GPS-denied region. The research focuses on a development and investigation of utilizing a camera to simultaneously determine a drone’s state while locating landmarks, where there is uncertainty in the landmarks’ exact positions prior to the mission (SLAM). This SLAM method is performed in regions with limited access to GPS. Furthermore, there is development and investigation of controlling the drone in conjunction with SLAM using potential error-reducing control parameters. Objectives are to quantitatively understand the UAV’s sensitivity of position errors to sensor grade and landmark characteristics as well as sensitivity of position errors to tuned control parameters
Input design for identification of aircraft stability and control derivatives
An approach for designing inputs to identify stability and control derivatives from flight test data is presented. This approach is based on finding inputs which provide the maximum possible accuracy of derivative estimates. Two techniques of input specification are implemented for this objective - a time domain technique and a frequency domain technique. The time domain technique gives the control input time history and can be used for any allowable duration of test maneuver, including those where data lengths can only be of short duration. The frequency domain technique specifies the input frequency spectrum, and is best applied for tests where extended data lengths, much longer than the time constants of the modes of interest, are possible. These technqiues are used to design inputs to identify parameters in longitudinal and lateral linear models of conventional aircraft. The constraints of aircraft response limits, such as on structural loads, are realized indirectly through a total energy constraint on the input. Tests with simulated data and theoretical predictions show that the new approaches give input signals which can provide more accurate parameter estimates than can conventional inputs of the same total energy. Results obtained indicate that the approach has been brought to the point where it should be used on flight tests for further evaluation
Modern control concepts in hydrology
Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise
Statistical inference in radio astronomy
This thesis unifies several studies, which all are dedicated to the subject of statistical data
analysis in radio astronomy and radio astrophysics.
Radio astronomy, like astronomy as a whole, has undergone a remarkable development in the past twenty years in introducing new instruments and technologies. New telescopes like the upgraded VLA, LOFAR, or the SKA and its pathfinder missions offer unprecedented sensitivities, previously uncharted frequency domains and unmatched survey capabilities.
Many of these have the potential to significantly advance the science of radio astrophysics and cosmology on all scales, from solar and stellar
physics, Galactic astrophysics and cosmic magnetic fields, to Galaxy cluster astrophysics
and signals from the epoch of reionization.
Since then, radio data analysis, calibration and imaging techniques have entered a similar phase of new development to push the boundaries and adapt the field to the new instruments and scientific opportunities.
This thesis contributes to these greater developments in two specific subjects, radio interferometric imaging and cosmic magnetic field statistics.
Throughout this study, different data analysis techniques are presented and employed
in various settings, but all can be summarized under the broad term of statistical infer-
ence. This subject encompasses a huge variety of statistical techniques, developed to solve
problems in which deductions have to be made from incomplete knowledge, data or measurements. This study focuses especially on Bayesian inference methods that make use of a subjective definition of probabilities, allowing for the expression of probabilities and statistical knowledge prior to an actual measurement.
The thesis contains two different sets of application for such techniques. First, situations
where a complicated, and generally ill-posed measurement problem can be approached by
assuming a statistical signal model prior to infer the desired measured variable. Such
a problem very often is met should the measurement device take less data then needed
to constrain all degrees of freedom of the problem. The principal case investigated in
this thesis is the measurement problem of a radio interferometer, which takes incomplete
samples of the Fourier transformed intensity of the radio emission in the sky, such that it is
impossible to exactly recover the signal. The new imaging algorithm RESOLVE is presented,
optimal for extended radio sources. A first showcase demonstrates the performance of the
new technique on real data. Further, a new Bayesian approach to multi-frequency radio
interferometric imaging is presented and integrated into RESOLVE.
The second field of application are astrophysical problems, in which the inherent stochas-
tic nature of a physical process demands a description, where properties of physical quanti-
ties can only be statistically estimated. Astrophysical plasmas for instance are very often in a turbulent state, and thus governed by statistical hydrodynamical laws. Two studies are presented that show how properties of turbulent plasma magnetic fields can be inferred from radio observations
The Telecommunications and Data Acquisition Report
This quarterly publication provides archival reports on developments in programs in space communications, radio navigation, radio science, and ground-based radio and radar astronomy. It reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standardization activities at the Jet Propulsion Laboratory for space data and information systems
Application of modern control and nonlinear estimation techniques
Control and nonlinear estimation techniques applied to optimal guidance of low thrust spacecraft, planetary soft landings, and feedback systems desig
- …