Final Report Summary - MOBILE (Modelling, Optimization and Control of Biomedical Systems)
The aim of the ERC MOBILE project is to develop individualised computer-based therapeutic advisors that provide optimal treatment regimens in the context of the biomedical applications of Anaesthesia, Type 1 Diabetes and Leukaemia.
The design of the therapeutic advisors/controllers was approached using the following methodology: Firstly, individualised physiologically based models of drug distribution and uptake, pharmacokinetics, drug effect and pharmacodynamics have been developed for the cases of Anaesthesia, Type 1 Diabetes, Leukaemia and HIV. The models reliability and flexibility has been evaluated with various analysis studies (sensitivity analysis, identifiability etc). The validation of the models with clinical data shows a good prediction of the drug delivery systems.
Secondly, complexity reduction methodologies for the application of multi-parametric/explicit model predictive (mp-MPC) control to complex high fidelity models have been developed. The application of mp-MPC to complex nonlinear systems is of critical importance for the biomedical systems. The first step is concerned with the adaptation and development of model order reduction (MOR) techniques for application in combination to mp-MPC algorithms.
Then, different topics in multi-parametric programming and explicit model predictive control, with particular emphasis on hybrid systems have been investigated. The main goal was to extend the applicability of these concepts to a wider range of problems of practical interest, and to propose algorithmic solutions to challenging problems such as constrained dynamic programming of hybrid linear systems and nonlinear explicit model predictive control.
And finally, a first hardware on a chip was implemented as a basis at a fuel cell lab and demonstrated for a fuel cell powered car.
The design of the therapeutic advisors/controllers was approached using the following methodology: Firstly, individualised physiologically based models of drug distribution and uptake, pharmacokinetics, drug effect and pharmacodynamics have been developed for the cases of Anaesthesia, Type 1 Diabetes, Leukaemia and HIV. The models reliability and flexibility has been evaluated with various analysis studies (sensitivity analysis, identifiability etc). The validation of the models with clinical data shows a good prediction of the drug delivery systems.
Secondly, complexity reduction methodologies for the application of multi-parametric/explicit model predictive (mp-MPC) control to complex high fidelity models have been developed. The application of mp-MPC to complex nonlinear systems is of critical importance for the biomedical systems. The first step is concerned with the adaptation and development of model order reduction (MOR) techniques for application in combination to mp-MPC algorithms.
Then, different topics in multi-parametric programming and explicit model predictive control, with particular emphasis on hybrid systems have been investigated. The main goal was to extend the applicability of these concepts to a wider range of problems of practical interest, and to propose algorithmic solutions to challenging problems such as constrained dynamic programming of hybrid linear systems and nonlinear explicit model predictive control.
And finally, a first hardware on a chip was implemented as a basis at a fuel cell lab and demonstrated for a fuel cell powered car.