1. Determining Sets of Parameters to be Estimated for Nonlinear Model Predictive Control Juergen Hahn, McFerrin Dept. of Chemical Engineering, Texas A&M University
A common solution for improving accuracy of a process model is to estimate some of the model parameters from process data. However, the question of which model parameters should be estimated from data is often not systematically addressed and instead parameter set selection is performed based upon experience with the process. This practice can be problematic as determining a good set of parameters for estimation becomes less intuitive as more sophisticated models containing dozens or even hundreds of parameters are used, of which only a handful can be estimated from process data. An additional problem results from nonlinearity of the process. While techniques for parameter set selection exist for linear systems, they cannot take into account that the set of parameters to be estimated may depend upon operating conditions or even upon the nominal values of the parameters to be estimated.
The objective of the conducted work is to systematically address the issue of determining a set of parameters to be estimated. The procedure is specifically geared towards nonlinear systems as changing operating conditions, but also uncertainty in the nominal values of the parameters may change the importance of a chosen parameter set.