In this paper, we developed the parametric estimation and the self-tuning control problem of the nonlinear systems which are described by discrete-time nonlinear mathematical models, with unknown, time-varying parameters, and operative in a stochastic environment. The parametric estimation is realized by using the prediction error method and the recursive least squares techniques. The self-tuni...

In this paper, we consider the nonlinear system modelling problem for on-chip testing and diagnosis of embedded mixed-signal systems. A SituationDependent AutoRegressive model with eXogenous variable (SDARX) is introduced to approximate the conventional Nonlinear-ARX (NARX). The parameter search space is divided into a linear weight subspace and the nonlinear parameter subspace. A nonlinear par...

This paper studies fuzzy filtering design for a class of nonlinear discrete-time uncertain stochastic systems. First, the Takagi and Sugeno fuzzy model is proposed to approximate a nonlinear discrete-time uncertain stochastic system. Next, based on the fuzzy model, the fuzzy estimation for nonlinear discrete-time uncertain stochastic systems is studied. Using a suboptimal approach, the fuzzy es...

In this paper, we propose a suboptimal moving horizon estimator for general class of nonlinear systems. For the stability analysis, transfer "feasibility-implies-stability/robustness" paradigm from model predictive control to context estimation in following sense: Using suitably defined, feasible candidate solution based on an auxiliary observer, robust proposed is inherited independently lengt...