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Robust stability and performance analysis of 2D mixed continuous-discrete-time systems with uncertainty

Graziano Chesi, +1 more
- 01 May 2016 - 
- Vol. 67, Iss: 67, pp 233-243
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TLDR
This paper investigates 2D mixed continuous-discrete-time systems whose coefficients are polynomial functions of an uncertain vector constrained into a semialgebraic set and shows that a nonconservative linear matrix inequality condition for ensuring robust stability can be obtained by introducing complex Lyapunov functions depending polynomially on the uncertain vector and a frequency.
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This article is published in Automatica.The article was published on 2016-05-01 and is currently open access. It has received 49 citations till now. The article focuses on the topics: Lyapunov function & Semialgebraic set.

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Citations
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Journal ArticleDOI

$\mathcal H_{\infty }$ Control for 2-D Markov Jump Systems in Roesser Model

TL;DR: In this article, the authors considered the problem of asynchronous control for two-dimensional (2-D) Markov jump systems, where the hidden Markov model was employed when dealing with the asynchronization between a controlled system and a controller, and the relation between them was constructed through a conditional probability matrix.
Journal ArticleDOI

On robust Kalman filter for two-dimensional uncertain linear discrete time-varying systems: A least squares method

TL;DR: It is proved that the solution to a certain deterministic regularized least squares problem constrained by the nominal two-dimensional system model is equivalent to the generalized two- dimensional Kalman filter.
Journal ArticleDOI

Exponential Stability of Two-Dimensional Homogeneous Monotone Systems With Bounded Directional Delays

TL;DR: It is shown that based on the monotone property and homogeneity of the associated vector fields, necessary and sufficient delay-independent exponential stability conditions are derived and the magnitudes of delays are taken into deriving an explicit estimation of the exponential decay rate.
Journal ArticleDOI

Krein-space based robust H∞ fault estimation for two-dimensional uncertain linear discrete time-varying systems

TL;DR: By introducing an equivalent auxiliary system and a new certain indefinite quadratic form performance function, the system uncertainty can be appropriately considered into the new performance function and the fault estimator design is converted to the minimization problem of a quadRatic form.
Journal ArticleDOI

Comments on “On Stabilization of 2-D Roesser Models”

TL;DR: It is possible to find examples for which the above-mentioned condition for state feedback stabilisation introduces a very weak conservatism and hence it is not necessary, and the source of the conservatism has been identified.
References
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Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones

TL;DR: This paper describes how to work with SeDuMi, an add-on for MATLAB, which lets you solve optimization problems with linear, quadratic and semidefiniteness constraints by exploiting sparsity.
Journal ArticleDOI

A discrete state-space model for linear image processing

TL;DR: In this paper, the linear time-discrete state-space model is generalized from single-dimensional time to two-dimensional space, which includes extending certain basic known concepts from one to two dimensions, such as the general response formula, state transition matrix, Cayley-Hamilton theorem, observability, and controllability.
Journal ArticleDOI

Doubly-indexed dynamical systems: State-space models and structural properties

TL;DR: Doubly-indexed dynamical systems provide a state space realization of two-dimensional filters which includes previous state models, and algebraic criteria for testing structural properties (reachability, observability, internal stability) are introduced.
Proceedings ArticleDOI

Introducing SOSTOOLS: a general purpose sum of squares programming solver

TL;DR: The paper provides an overview on sum of squares programming, describes the primary features of SOSToolS, and shows how SOSTOOLS is used to solve sum of square programs.
Journal ArticleDOI

A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control

TL;DR: Considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts and to demonstrate the effectiveness of the proposed method.
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Frequently Asked Questions (15)
Q1. What have the authors contributed in "Robust stability and performance analysis of 2d mixed continuous-discrete-time systems with uncertainty" ?

This paper investigates 2D mixed continuous-discrete-time systems whose coefficients are polynomial functions of an uncertain vector constrained into a semialgebraic set. 

Several directions can be explored in future works. In particular, one could investigate the possibility of generalizing the results proposed in this paper to marginally stable systems. Another possibility could be the extension of the proposed results to systems with time-varying uncertainties. A last possibility could be the extension to the design of robust controllers. 

The proposed LMI conditions are based on the introduction of complex Lyapunov functions depending polynomially or rationally on a frequency and polynomially on the uncertainty. 

This paper has proposed LMI conditions for establishing robust exponential stability and for determining the robust H∞ and H2 norms of 2D mixed continuous-discrete-time systems whose coefficients are polynomial functions of an uncertain vector constrained into a semialgebraic set. 

The study of 2D mixed continuous-discrete-time systems has a long history, with some early works such as [1,2] introducing basic models, systems theory and stability properties. 

The LMI condition provided by Theorem 2 is sufficient for any chosen degrees of V (ω, p), Ti(ω, p) and Ui(ω, p), and it is also necessary for sufficiently large degrees under the assumption that P is strongly compact. 

Since establishing whether a complex matrix polynomials is SOS is equivalent to an LMI as explained in Section 2.2, and since X(ω, p) and Y (ω, p) are affine linear matrix functions of the decision variables ζ, V (ω, p), Ti(ω, p) and Ui(ω, p), it follows that the condition (32) amounts to solving an LMI feasibility test. 

0. (35)Consequently, one has that(1)–(3) is robustly exponentially stable m∃d : ζ∗ > 0. (36)The optimization problem (34), which amounts to minimizing a linear function subject to SOS constraints and linear equations, is a convex optimization problem, in particular a semidefinite program (SDP). 

The LMI condition provided by Theorem 3 is sufficient for any chosen degrees of V (ω, p), Ti(ω, p) and Ui(ω, p), and it is also necessary for sufficiently large degrees under the assumption that P is strongly compact. 

Let us observe that the condition trace(V (ω0, p0)) = 1 can be introduced without loss of generality since V (ω, p) and ζ are defined up to a positive scale factor in the LMI condition provided by Theorem 2. 

This matrix polynomial is said to be SOS if there exist real matrix polynomials Ji(ω, p), i = 1, . . . , l, such thatJ(ω, p) =l ∑i=1Ji(ω, p) TJi(ω, p). (12)A necessary and sufficient condition for establishing whether J(ω, p) is SOS can be obtained via an LMI feasibility test. 

From the definition of SOS matrix polynomials in Section 2.2, the first constraint in (32) implies thatX(ω, p) ≥ 0 ∀ω ∈ R ∀p ∈ Rq.Similarly, one obtains that Y (ω, p), Ti(ω, p) and Ui(ω, p) are positive semidefinite for all ω ∈ R for all p ∈ Rq. Next, let ω0 ∈ R and p0 ∈ P be arbitrary values. 

The system (1)–(3) is robustly exponentially stable if and only if|λi(F1(jω, p))| < 1 ∀i = 1, . . . , nd ∀ω ∈ R ∀p ∈ P. (25)This lemma can be exploited to derive a condition for robust exponential stability of the system (1)–(3) through the use of suitable Lyapunov function candidates. 

Researchers have investigated several fundamental properties of 2D mixed continuous-discrete-time systems, in particular stability, for which key contributions include [3,6–9]. 

I− na ∑i=1ai(p)Ui(ω, p).(33)Moreover, if P is strongly compact, this condition is not only sufficient but also necessary for (1)–(3) to be robustly exponentially stable.