Estimation under model uncertainty

Ellipsoidal Estimation under Model Uncertainty

Ellipsoidal outer-bounding under model uncertainty is a natural extension of state estimation for models with unknown-but-bounded errors. The technique described in this paper applies to linear discrete-time dynamic systems. Many difficulties arise because of the non-convexity of feasible sets. Analytical optimal or suboptimal solutions are presented, which are counterparts in this context of u...

Interval Technique for Parameter Estimation under Model Uncertainty

This paper is devoted to the problem of estimation of parameters for linear multioutput models with uncertain regressors and additive noise. The uncertainty is assumed to be described by intervals. Outer-bounding interval approximations of the non-convex feasible parameter set for uncertain system are obtained. The proposed method is based on the calculation of the interval solution for an inte...

Probabilistic Optimal Estimation under Uncertainty

The classical approach to system identification is based on stochastic assumptions about the measurement error, and provides estimates that have random nature. Worst-case identification, on the other hand, only assumes the knowledge of deterministic error bounds, and establishes guaranteed estimates, thus being in principle better suited for the use in control design. However, a main limitation...

State estimation under uncertainty: A survey

Probabilistic Optimal Estimation and Filtering under Uncertainty

The classical approach to system identification is based on stochastic assumptions about the measurement error, and provides estimates that have random nature. Worst-case identification, on the other hand, only assumes the knowledge of deterministic error bounds, and establishes guaranteed estimates, thus being in principle better suited for the use in control design. However, a main limitation...