SGER: Model Uncertainty and Robustness in Nonlinear Model Predictive Control for Biomedical Applications

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Intellectual Merit:An issue in the development of any process control strategy is the sensitivity and robustness of the process models and the application of viable computational procedures where the input is uncertain information or about processes or specific events. This SGER project is exploratory research aimed at exploring sensitivity and robustness issues in process control with biomedical applications. In addition, a better understanding of the effects of uncertainties and the development of more robust design and analysis procedures could also benefit industry through enhanced product effectiveness and better-trained engineers. New uncertainty-analysis methodologies might also be used by regulatory agencies in the approval of new procedures and technologies in medicine. The main goal of the project is to explore the robustness due to uncertainties in predictive control algorithms, ultimately for biomedical applications - specifically exploring the robustness of nonlinear model predictive control (MPC) in the presence of model uncertainty. Some case studies, such as insulin delivery systems, will ultimately be analyzed.Broad Impact:The research has the potential to influence the way medication is administered to patients. Drugs might be administered to match the patient's needs, rather than according to a prescribed schedule. Applications run the gamut from controlling insulin dosages to the administering of various drugs for cancer treatment.