An extension of this method is proposed which enables to design PID controller guaranteeing robust stability and nominal performance for the uncertain plant modeled using unstructured uncertainty.
The original benefits model used uncertainty in the reduction of BPb over the period studied (variable A) and the IQ BPb slope (term B) to calculate upper and lower bounds on economic benefits.
The underlying uncertainty is modeled using probabilistic theory.
Listing 3 shows an example where uncertainty is modeled using probabilistic and conditional rules.
This stiffness matrix is modeled using a mixed nonparametric parametric stochastic model in which the nonparametric (maximum entropy) component is used to model the uncertainty in final shape while the preload is explicitly, parametrically included in the stiffness matrix representation.
In the face of parameter uncertainty, modeling using Boolean networks can produce qualitative predictions at the right level of detail for many applications.
Model performance was ascertained using uncertainty analysis on both the parameters, k1 and k2, for each of the five models.
It is proved that the possibility-constrained model can be transformed to an equivalent deterministic transportation model using inverse uncertainty distribution.
These experiments are typically analyzed using a heuristics-based curve classification algorithm that does not use uncertainty in model fits to make activity calls (Inglese et al. 2006).
To generate our predictions we analyzed the model using time-dependent uncertainty analyses [ 1- 5], [ 11- 13].
