The technique has been successfully applied to a four state, four output nonlinear CSTR model. The system model exhibits a gain change and both minimum and nonminimum phase behavior. The estimation technique is run with 10 different parameter variations on a horizon of length 25. Currently, the size of the problem s continuous variable scales linearly with respect to estimation horizon, process parameters, process outputs, and number of linear models used. The number of integer variables is equal to the number of faults in the system.
Computation time is on the order of one minute.
This is approximately the system sampling rate. Algorithm engineering will allow reduction in the expected compute time. Banergee, Arkun, Ogunnaike, and Pearson, Estimation of Nonlinear Systems Using Linear Multiple Models, AIChE 1997. Kozub and Mcgregor, State Estimation for Semi-Batch Polymerization Reactors, 1992. Muske and Edgar, Nonlinear Process Control, 1997.