Direct virtual sensors

Optimal estimators (or observers or filters) for nonlinear systems are in general difficult to derive or implement. The common approach is to use approximate solutions such as extended Kalman filters, ensemble filters or particle filters. However, no optimality properties can be guaranteed by these approximations, and even the stability of the estimation error cannot often be ensured. Another relevant issue is that, in most practical situations, the system whose variables have to be estimated is not known, and a two-step procedure is adopted, based on model identification from data and filter design from the identified model. However, the designed filter may display large performance deteriorations in the case of modeling errors. Within this activity, a novel approach overcoming these issues is being developed, allowing the design of optimal filters for nonlinear systems in both the cases of known and unknown system. The approach is based on the direct filter identification from a set of data generated by the system.  One aspect of this research activity is to consider some of the techniques in the machine learning field to improve the numerical efficiency of the design algorithms.

ERC Sector:

  • PE7_1 Control engineering
  • PE7_7 Signal processing
  • PE1_19 Control theory and optimisation

Keywords:

  • Filtering
  • Observers
  • Data-driven observer

Research groups