In this paper a generalization of avoidance control for multi-agent dynamic systems is presented. Strategies for avoidance control for multiple agents are obtained using individual Liapunov-type functions. The overall system avoidance conditions are guaranteed using a vector Liapunov-type function via an M-matrix property.

This paper is concerned with a cyclic predator-prey system with Sigmoidal type functional response. By using the differential inequality theory, some sufficient conditions are derived for the permanence of the system. By constructing a suitable Liapunov function, we obtain that the system has a unique asymptotically periodic solution which is globally asymptotically stable. Some numerical simul...

This paper discusses asymptotic stability for nonautonomous systems by means of the direct method of Liapunov. The existence of a positive time-invariant Liapunov function with negative semi-definite derivative is assumed. The paper focuses on the extra conditions needed in order to guarantee asymptotic stability. The proposed criterion is compared with the standard results available for autono...

Artificial neural networks for solving different variants of linear programming problems are proposed and analyzed through Liapunov direct method. An energy function with an exact penalty term is associated to each variant and leads to a discontinuous dynamic gradient system model of an artificial neural network. The objective is to derive conditions that the network gains must satisfy in order...

An infectious disease model with latent infection is studied, and the basic reproductio n number of obtained When reproducing less than 1, mode l has only one disease-free equilibrium. greater t he equilibrium unstable. The also a unique positive global asymptotic stability two equilibria analyzed by linearization method, Liapunov function method geometric method.

Cauchy problem, 19, 136Condition number, 40, 55, 99, 110Contractibility, 183Counterflow equation, 156Critical delay, 167Delay Liapunov function, 138Delay margin, 170Departure from normality, 56Diagonalizable matrix, 55Diagonally dominant matrix, 36Differential inclusion, 45Dissipativity radius, 33Dual norm, 8, 25Dual pair, 25Eccentricity, ...

In these notes we discuss Markov processes, in particular stochastic differential equations (SDE) and develop some tools to analyze their long-time behavior. There are several ways to analyze such properties, and our point of view will be to use systematically Liapunov functions which allow a nice characterization of the ergodic properties. In this we follow, at least in spirit, the excellent b...