GloptiPoly is a Matlab/SeDuMi add-on to build and solve convex linear matrix inequality (LMI) relaxations of non-convex optimization problems with multivariate polynomial objective function and constraints, based on the theory of moments. In contrast with the dual sum-of-squares decompositions of positive polynomials, the theory of moments allows to detect global optimality of an LMI relaxation...

The robust stochastic stability for a class of uncertain neutral-type delayed neural networks driven by Wiener process is investigated. By utilizing the Lyapunov-Krasovskii functional and inequality technique, some sufficient criteria are presented in terms of linear matrix inequality LMI to ensure the stability of the system. A numerical example is given to illustrate the applicability of the ...

In this paper, the problemof robust exponential stability analysis of uncertain discrete-time recurrent neural networks withMarkovian jumping and time-varying delays is studied. By employing the Lyapunov functional and linear matrix inequality (LMI) approach, a new sufficient criterion is proposed for the global robust exponential stability of discrete-time recurrent neural networks which conta...

Abstract Under the multi-objective framework, this paper presents a hybrid algorithm to solve robust static output feedback control problem for continuous polytopic uncertain system. To obtain static output feedback gain, a new hybrid algorithm is proposed by combination of a hybrid algorithm of the Particle Swarm Optimization (PSO) and Differential Evolution (DE), and the linear matrix inequal...

this paper presents a new observer design methodology for a time varying actuator fault estimation. a new linear matrix inequality (lmi) design algorithm is developed to tackle the limitations (e.g. equality constraint and robustness problems) of the well known so called fast adaptive fault estimation observer (fafe). the fafe is capable of estimating a wide range of time-varying actuator fault...

in this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the takagi-sugeno (t-s) fuzzy models is considered. a novel linear matrix inequality (lmi)-based stability criterion is obtained by using lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

On the basis of a parameterization of the span of a multivariate matrix polynomial, a sufficient condition is derived in this paper for a multi-dimensional multi-input multi-output (MIMO) IIR system being upper bounded over a cuboid frequency domain. This condition is expressed through a linear matrix inequality (LMI) and can be computationally verified. Moreover, by means of parameter dependen...

In this paper, the simultaneous stabilization problem is considered using the matrix inequality approach. Some necessary and sufficient conditions for simultaneous stabilizability of r strictly proper multi-input/multi-output (MIMO) plants via static output feedback and state feedback are obtained in the form of coupled ARI’s. It is shown that any such stabilizing feedback gain is the solution ...

This paper deals with the problem of observer-based stabilization for linear systems with parameter inequality. A new design methodology is proposed thanks to a judicious use of the famous Young relation. This latter leads to a less restrictive synthesis condition, expressed in term of Linear Matrix Inequality (LMI), than those available in the literature. Numerical comparisons are provided in ...

Preserving Order Observers provide an estimation that is always above or below the true variable and, in the absence of uncertainties/perturbations, the estimation converges asymptotically to the true value of the variable. In this paper we propose a novel methodology to design preserving order observers for a class of nonlinear systems in the nominal case or when perturbations/uncertainties ar...