245 research outputs found
Detection of abrupt changes in dynamic systems
Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed
A survey of design methods for failure detection in dynamic systems
A number of methods for the detection of abrupt changes (such as failures) in stochastic dynamical systems were surveyed. The class of linear systems were emphasized, but the basic concepts, if not the detailed analyses, carry over to other classes of systems. The methods surveyed range from the design of specific failure-sensitive filters, to the use of statistical tests on filter innovations, to the development of jump process formulations. Tradeoffs in complexity versus performance are discussed
Estimation for bilinear stochastic systems
Three techniques for the solution of bilinear estimation problems are presented. First, finite dimensional optimal nonlinear estimators are presented for certain bilinear systems evolving on solvable and nilpotent lie groups. Then the use of harmonic analysis for estimation problems evolving on spheres and other compact manifolds is investigated. Finally, an approximate estimation technique utilizing cumulants is discussed
A class of finite dimensional optimal nonlinear estimators
Finite dimensional optimal nonlinear state estimators are derived for bilinear systems evolving on nilpotent and solvable Lie groups. These results are extended to other classes of systems involving polynomial nonlinearities. The concepts of exact differentials and path-independent integrals are used to derive optimal finite dimensional estimators for a further class of nonlinear systems
Control optimization, stabilization and computer algorithms for aircraft applications
The analysis and design of complex multivariable reliable control systems are considered. High performance and fault tolerant aircraft systems are the objectives. A preliminary feasibility study of the design of a lateral control system for a VTOL aircraft that is to land on a DD963 class destroyer under high sea state conditions is provided. Progress in the following areas is summarized: (1) VTOL control system design studies; (2) robust multivariable control system synthesis; (3) adaptive control systems; (4) failure detection algorithms; and (5) fault tolerant optimal control theory
Semidefinite descriptions of the convex hull of rotation matrices
We study the convex hull of , thought of as the set of
orthogonal matrices with unit determinant, from the point of view of
semidefinite programming. We show that the convex hull of is doubly
spectrahedral, i.e. both it and its polar have a description as the
intersection of a cone of positive semidefinite matrices with an affine
subspace. Our spectrahedral representations are explicit, and are of minimum
size, in the sense that there are no smaller spectrahedral representations of
these convex bodies.Comment: 29 pages, 1 figur
Rank-Sparsity Incoherence for Matrix Decomposition
Suppose we are given a matrix that is formed by adding an unknown sparse
matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix
into its sparse and low-rank components. Such a problem arises in a number of
applications in model and system identification, and is NP-hard in general. In
this paper we consider a convex optimization formulation to splitting the
specified matrix into its components, by minimizing a linear combination of the
norm and the nuclear norm of the components. We develop a notion of
\emph{rank-sparsity incoherence}, expressed as an uncertainty principle between
the sparsity pattern of a matrix and its row and column spaces, and use it to
characterize both fundamental identifiability as well as (deterministic)
sufficient conditions for exact recovery. Our analysis is geometric in nature,
with the tangent spaces to the algebraic varieties of sparse and low-rank
matrices playing a prominent role. When the sparse and low-rank matrices are
drawn from certain natural random ensembles, we show that the sufficient
conditions for exact recovery are satisfied with high probability. We conclude
with simulation results on synthetic matrix decomposition problems
A dual-mode generalized likelihood ratio approach to self-reorganizing digital flight control system design
The research is reported on the problems of failure detection and reliable system design for digital aircraft control systems. Failure modes, cross detection probability, wrong time detection, application of performance tools, and the GLR computer package are discussed
Redundancy relations and robust failure detection
All failure detection methods are based on the use of redundancy, that is on (possible dynamic) relations among the measured variables. Consequently the robustness of the failure detection process depends to a great degree on the reliability of the redundancy relations given the inevitable presence of model uncertainties. The problem of determining redundancy relations which are optimally robust in a sense which includes the major issues of importance in practical failure detection is addressed. A significant amount of intuition concerning the geometry of robust failure detection is provided
Diagonal and Low-Rank Matrix Decompositions, Correlation Matrices, and Ellipsoid Fitting
In this paper we establish links between, and new results for, three problems
that are not usually considered together. The first is a matrix decomposition
problem that arises in areas such as statistical modeling and signal
processing: given a matrix formed as the sum of an unknown diagonal matrix
and an unknown low rank positive semidefinite matrix, decompose into these
constituents. The second problem we consider is to determine the facial
structure of the set of correlation matrices, a convex set also known as the
elliptope. This convex body, and particularly its facial structure, plays a
role in applications from combinatorial optimization to mathematical finance.
The third problem is a basic geometric question: given points
(where ) determine whether there is a centered
ellipsoid passing \emph{exactly} through all of the points.
We show that in a precise sense these three problems are equivalent.
Furthermore we establish a simple sufficient condition on a subspace that
ensures any positive semidefinite matrix with column space can be
recovered from for any diagonal matrix using a convex
optimization-based heuristic known as minimum trace factor analysis. This
result leads to a new understanding of the structure of rank-deficient
correlation matrices and a simple condition on a set of points that ensures
there is a centered ellipsoid passing through them.Comment: 20 page
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