764 research outputs found
Block-Diagonal Solutions to Lyapunov Inequalities and Generalisations of Diagonal Dominance
Diagonally dominant matrices have many applications in systems and control
theory. Linear dynamical systems with scaled diagonally dominant drift
matrices, which include stable positive systems, allow for scalable stability
analysis. For example, it is known that Lyapunov inequalities for this class of
systems admit diagonal solutions. In this paper, we present an extension of
scaled diagonally dominance to block partitioned matrices. We show that our
definition describes matrices admitting block-diagonal solutions to Lyapunov
inequalities and that these solutions can be computed using linear algebraic
tools. We also show how in some cases the Lyapunov inequalities can be
decoupled into a set of lower dimensional linear matrix inequalities, thus
leading to improved scalability. We conclude by illustrating some advantages
and limitations of our results with numerical examples.Comment: 6 pages, to appear in Proceedings of the Conference on Decision and
Control 201
Some Applications of Polynomial Optimization in Operations Research and Real-Time Decision Making
We demonstrate applications of algebraic techniques that optimize and certify
polynomial inequalities to problems of interest in the operations research and
transportation engineering communities. Three problems are considered: (i)
wireless coverage of targeted geographical regions with guaranteed signal
quality and minimum transmission power, (ii) computing real-time certificates
of collision avoidance for a simple model of an unmanned vehicle (UV)
navigating through a cluttered environment, and (iii) designing a nonlinear
hovering controller for a quadrotor UV, which has recently been used for load
transportation. On our smaller-scale applications, we apply the sum of squares
(SOS) relaxation and solve the underlying problems with semidefinite
programming. On the larger-scale or real-time applications, we use our recently
introduced "SDSOS Optimization" techniques which result in second order cone
programs. To the best of our knowledge, this is the first study of real-time
applications of sum of squares techniques in optimization and control. No
knowledge in dynamics and control is assumed from the reader
Distance to the Nearest Stable Metzler Matrix
This paper considers the non-convex problem of finding the nearest Metzler
matrix to a given possibly unstable matrix. Linear systems whose state vector
evolves according to a Metzler matrix have many desirable properties in
analysis and control with regard to scalability. This motivates the question,
how close (in the Frobenius norm of coefficients) to the nearest Metzler matrix
are we? Dropping the Metzler constraint, this problem has recently been studied
using the theory of dissipative Hamiltonian (DH) systems, which provide a
helpful characterization of the feasible set of stable matrices. This work uses
the DH theory to provide a block coordinate descent algorithm consisting of a
quadratic program with favourable structural properties and a semidefinite
program for which recent diagonal dominance results can be used to improve
tractability.Comment: To Appear in Proc. of 56th IEEE CD
Sparse sum-of-squares (SOS) optimization: A bridge between DSOS/SDSOS and SOS optimization for sparse polynomials
Optimization over non-negative polynomials is fundamental for nonlinear
systems analysis and control. We investigate the relation between three
tractable relaxations for optimizing over sparse non-negative polynomials:
sparse sum-of-squares (SSOS) optimization, diagonally dominant sum-of-squares
(DSOS) optimization, and scaled diagonally dominant sum-of-squares (SDSOS)
optimization. We prove that the set of SSOS polynomials, an inner approximation
of the cone of SOS polynomials, strictly contains the spaces of sparse
DSOS/SDSOS polynomials. When applicable, therefore, SSOS optimization is less
conservative than its DSOS/SDSOS counterparts. Numerical results for
large-scale sparse polynomial optimization problems demonstrate this fact, and
also that SSOS optimization can be faster than DSOS/SDSOS methods despite
requiring the solution of semidefinite programs instead of less expensive
linear/second-order cone programs.Comment: 9 pages, 3 figure
H∞ fuzzy control for systems with repeated scalar nonlinearities and random packet losses
Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the H∞ fuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi-Sugeno (T-S) fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as recurrent neural networks. The measurement transmission between the plant and controller is assumed to be imperfect and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to represent the phenomenon of random packet losses. Attention is focused on the analysis and design of H∞ fuzzy controllers with the same repeated scalar nonlinearities such that the closed-loop T-S fuzzy control system is stochastically stable and preserves a guaranteed H∞ performance. Sufficient conditions are obtained for the existence of admissible controllers, and the cone complementarity linearization procedure is employed to cast the controller design problem into a sequential minimization one subject to linear matrix inequalities, which can be readily solved by using standard numerical software. Two examples are given to illustrate the effectiveness of the proposed design method
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