Interval numerical observer: Application to a discrete time nonlinear fish model

International audienceThe aim of this work is to reconstitute the state of a discrete-time nonlinear system representing a dynamical model of a harvested fish population. For this end, we are going to use a numerical method of building an interval observer for the consider discrete-time model fish population. We adapt to this model an algorithm called "Interval Moving Horizon State Estimation" (IMHSE) which gives an estimated interval of the system states. This algorithm is carried out in [8] and work well for a general class of discrete-time systems.Le but de ce travail est de reconstruire les états d’un système discret non linéaire représentant la dynamique d’une population de poissons soumise à l’action de la pêche. Pour cela nous allons utiliser une méthode numérique de synthèse d’un observateur intervalle du modèle discret de la population de poissons considéré. Nous adaptons à ce modèle un algorithme appelé "Interval Moving Horizon State Estimation" (IMHSE) qui permet d’estimer les états du système par des intervalles. Cet algorithme est développé dans [8] et marche bien pour une classe générale de systèmes discrets

An exponential observer for discrete-time systems with bilinear drift and rational output functions

Abstract-This paper presents an observer with exponential error decay for systems described by bilinear input-state dynamics and output functions that are ratios of polynomials in the state. It is shown that such kind of systems can be immersed into systems of higher dimension, with time-varying linear state dynamics and linear output map. The observer here presented is derived exploiting the structure of the extended system. Conditions of global exponential convergence are given and discussed. Computer simulations demonstrate good behavior of the observer, even in the presence of disturbances on the state and output equations

An observer for a nonlinear age-structured model of a harvested fish population.

International audienceWe consider an age-structured model of a harvested population. This model is a discrete-time system that includes a nonlinear stock-recruitment relationship. Our purpose is to estimate the stock state. To achieve this goal, we built an observer, which is an auxiliary system that uses the total number of fish caught over each season and gives a dynamical estimation of the number of fish by age class. We analyse the convergence of the observer and we show that the error estimation tends to zero with exponential speed if a condition on the fishing effort is satisfied. Moreover the constructed observer (dynamical estimator) does not depend on the poorly understood stock-recruitment relationship. This study shows how some tools from nonlinear control theory can help to deal with the state estimation problem in the field of renewable resource management