Preferential Estimation for Uncertain Linear Systems at Steady State: Application to Filamentous Fungal Fermentation

State estimation is a widely used concept in the control community, and the literature mostly concentrates on the estimation of all states. However, in soft sensor problems, the emphasis is on estimating a few soft outputs as accurately as possible. The concept of preferential estimation consists of estimating these soft outputs more accurately than the other states. The main question is whether or not the accuracy along the soft outputs can be improved, possibly at the detriment of other states. This papers shows that, though preferential estimation is not possible for linear systems with perfect model information and gaussian process and measurement noises, it is indeed possible for linear systems with model uncertainty. The theoretical concepts are illustrated on a filamentous fungal fermentation.