State estimation and fault detection of uncertain systems based on an interval approach

This paper deals with state estimation and fault detection in the presence of unknown but bounded state perturbations and measurement noise. In this context, most available results are for linear models. Based on interval analysis, a state estimator for nonlinear dynamical systems is presented. Given the perturbation and noise bounds, the proposed method evaluates a set estimate guaranteed to contain all values of the state that are consistent with the available observations. The estimator is then used to regime shift detection. A numerical example is given.