We 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 c...

This paper is concerned with the problem of almost sure state estimation for general nonlinear hybrid stochastic systems whose coefficients only satisfy local Lipschitz conditions. By utilizing the stopping time method combined with martingale inequalities, a theoretical framework is established for analyzing the so-called almost surely asymptotic stability of the addressed system. Within such ...

We propose a framework for designing observers noisy nonlinear systems with global convergence properties and performing robustness noise sensitivity. This comes out from the combination of state norm estimator chain filters, adaptively tuned by estimator. The estimate is sequentially processed through filters. Each filter contributes to improving, certain amount, estimation error performances ...

Measuring the state variables of systems actuated by shape memory alloys (SMAs) is normally a difficult task because of the small diameter of the SMA wires. In such cases, as an alternative, observers are used to estimate the state vector. This paper presents an extended Kalman filter (EKF) for estimation of the state variables of a single-degree-of-freedom rotary manipulator actuated by an SMA...

This paper presents a filter approach for estimating the state of nonlinear dynamic systems based on recursive approximation of posterior densities by means of Dirac mixture functions. The filter consists of a prediction step and a filter step. The approximation approach is based on a systematic minimization of a distance measure and is hence optimal and deterministic. In contrast to non-determ...

in this paper a novel hybrid algorithm for harmonics estimation in power systems is proposed. the estimation of the harmonic components is a nonlinear problem due to the nonlinearity of phase of sinusoids in distorted waveforms. most researchers implemented nonlinear methods to extract the harmonic parameters. however, nonlinear methods for amplitude estimation increase time of convergence. hen...

This work studies the state estimation problem of a stochastic nonlinear system with unknown sensor measurement losses. If the estimator knows the sensor measurement losses of a linear Gaussian system, the minimum variance estimate is easily computed by the celebrated intermittent Kalman filter (IKF). However, this will no longer be the case when the measurement losses are unknown and/or the sy...