Special issue on interval estimation applied to diagnosis and control of uncertain systems

Abstract

The estimation of unmeasured state and parameters for complex systems is of great importance for applications of the control theory. Emerging applying domains require reliable approaches on the estimator design for diverse classes of dynamical systems. In many areas, as in biology, for example, the intrinsic uncertainty of the models prevents for applications of conventional estimation approaches. Uncertainty can be represented by unknown inputs/disturbances, noises, parameters or resulting from a transformation of nonlinear systems using Linear Parameter-Varying (LPV) tools, where strong nonlinearities are modelled by uncertain parameters. In some cases, an exact estimation of the state is not possible due to a lack of information about uncertainties. In such cases, set-membership techniques can be used to provide not only an approximation but also a set covering all the values of the state/parameters consistent with the available measurements.