An evaluation – which is at the same time absolute and efficient – of the uncertainty of the model chosen as a foundation of the control design is never possible.

In fact, since no mathematical model can give a complete and precise description of a plant, any model appears always widely uncertain in absolute terms.

For being  efficient the evaluation of the uncertainty should not be absolute, but relative to the specific aspects of the plant dynamics, which are of interest in connection with the specific control requirements established.

One aim of the EICAS methodology is an efficient evaluation of the model uncertainty.

After having chosen the model on which the design will be based (the so called approximate model) the designer builds a new model (called fine model) apt to show the validity limits of the first one concerning the expected performance of the control to be designed.

The fine model should not give necessarily a more precise description than the first one, but instead it should be extended up to including the modeling of behavioral phenomena of the plant which the first model willingly neglected and could possibly have influence on the control performance if it were extended beyond the limits established by the design requirements.

The fine model will be often non-linear, and will include an approximate description of the above mentioned behavioural phenomena of the plant.