Impact of system identification methods in metabolic modeling and control
Metabolic modelling can significantly improve control of hyperglycaemia. Clinical control demands physiological accuracy in identifying patient specific parameters. However, typically used non-linear and non-convex identification methods and models can deliver sub-optimal results, affecting control prediction. This research compares a typical non-linear method and a novel linear, convex method for an accepted metabolic control model using retrospective clinical control data. Results show increased errors in fitting for the non-linear fitting method. A significant (140-660X) increase in computational efficiency is also reported. The methods and results presented can be readily applied and generalised to a wider set of pharmacokinetic and pharmacodynamic systems that use similar linear and non-linear models.