We consider optimal estimation problems characterized by a state vector with i) dynamics described via a differential equation with Lipschitz nonlinearities, ii) partial information provided via a Lipschitz nonlinear mapping, and iii) an Lp norm measure of the estimation error to be minimized. An approximate solution of such optimal estimation problem is searched for by restricting the optimiza...

This paper deals with the state and unmeasured disturbance estimation within the nonlinear model predictive control of the superheated steam temperature in a once-trough boiler of a fossil fuelled power plant. The nonlinear model of the once-trough boiler has very high order, it is significantly nonlinear and load dependent. Model is excited by several disturbances, but not all of them are meas...

This paper presents a generalization of the Kalman filter for linear and nonlinear fractional order discrete state-space systems. Linear and nonlinear discrete fractional order state-space systems are also introduced. The simplified kalman filter for the linear case is called the fractional Kalman filter and its nonlinear extension is named the extended fractional Kalman filter. The background ...

A Current Output Observer is presented and its estimation error performance is compared to that of the Extended Kalman Filter. It is shown that performance improvement can be obtained by this new scheme with minor increase in computational load. In order to obtain stronger results, scalar nonlinear stochastic systems are focused on. These systems are categorized based on the derivatives of thei...

This paper presents a generalization of the Kalman Filter for linear and nonlinear fractional order discrete state-space systems. The linear and nonlinear discrete fractional order state-space systems are also introduced. The simplified Kalman Filter for linear case is called Fractional Kalman Filter and its nonlinear extension is named Extended Fractional Kalman Filter respectively. The backgr...

The estimation of parameters in nonlinear algebraic models through the error-in-variables method has been widely studied from a computational standpoint. The method involves the minimization of a weighted sum of squared errors subject to the model equations. Due to the nonlinear nature of the models used, the resulting formulation is nonconvex, and may contain several local minima in the region...

Nonlinear Bayesian filtering has been utilized in numerous fields and applications. One of the most popular class of Bayesian algorithms is Particle Filters. Their main benefit is the ability to estimate complex posterior density of the state space in nonlinear models. This paper presents the application of particle filtering to the problem of parameter estimation and calibration of a nonlinear...

State estimation and estimator based predictive control of nonlinear autonomous hybrid systems poses a challenging problem as these systems involve discontinuities that are introduced by switching of the discrete variables. In this paper, we propose a state estimation scheme for an autonomous hybrid system using an ensemble Kalman filter (EnKF), which belongs to the class of particle filters an...

Nonor semiparametric estimation and lag selection methods are proposed for three seasonal nonlinear autoregressive models of varying seasonal flexibility+All procedures are based on either local constant or local linear estimation+ For the semiparametric models, after preliminary estimation of the seasonal parameters, the function estimation and lag selection are the same as nonparametric estim...