Optimal Coordination Systems
Population-based approach & stochastic modeling of diabetics
Blood glucose concentrations show a very high variability for every individual, but also from one person to another. However a relatively accurate prediction of these concentrations is important for the treatment of persons with Type 1 Diabetes. The population-based approach uses data from a pool of patients to identify population as well as patient parameters of a model of the insulin-glucose system. This enables to consider more complex model structures, the intra- and inter-patient variability and to identify correlation between model parameters and other directly measurable patient data such as weight.
In previous projects, different population-based methods have been compared and a Matlab toolbox was developed to implement the combination of a stochastic model with a population-based approach. This implementation has mainly been tested with artificial data. The project consists in testing the approach using real clinical data. The results should include new model structures and improved approaches that allow more reliable predictions.
MER: Denis Gillet
Type of project: Master
Contact: Alain Bock and Gregory Francois
Optimal control for the treatment of diabetics during physical activity
Persons with Type 1 Diabetes have to almost continuously adapt their therapy and lifestyle to account for situations like meal intakes, exercise, illness, medication or stress. Patients using insulin pumps have the possibility to infuse time-varying continuous profiles. This flexibility is not fully exploited by the most commonly used rules to compensate for the above mentioned situations. In this project, we propose to focus on the physical exercise.
It is known that exercise reduces the insulin needs of the patient for a certain period of time. It is also known that this reduction depends on one side on the duration and intensity of the exercise and on the other side on every patient. A model accounting for the effect of physical activity has recently been developed.
The goal of this project is to improve this model and to estimate its parameters using real patient data. The identification should enable the individualization of the method. Eventually, the model coupled to the individual parameters will be used to compute optimal insulin and lifestyle recommendations.
This project is carried out in collaboration with a multinational pharmaceutical company.
MER: Denis Gillet
Type of project: Master
Contact: Alain Bock and Gregory Francois
Comfort Adaptation for Coordination of Autonomous Vehicles using Decentralized Navigation Functions
Automated Guided Vehicles (AGVs) operating on roads have a high potential for reducing CO2 emissions and traffic congestion in urban areas. Methods of crossing based on decentralized navigation function (DNF) have been developed and validated in MATLAB.
The main goal of this project is to adapt the present scenario of coordination of autonomous vehicles at intersections in order to take into account comfort issues. The student is supposed to first study the present criteria, which are used to improve the comfort of normal vehicles and autonomous vehicles. Second, he or she will introduce new terms in the navigation function to guarantee the comfort at intersections.
MER: Denis Gillet
Type of project: Master or Semester
Contact: Laleh Makarem
Graph-based Decentralized Control of Autonomous Vehicles on Predefined Paths
Electric Automated Guided Vehicles (AGVs) operating on roads have a high potential for reducing CO2 emissions and traffic congestion in inter modality areas. Advanced maneuvering solutions relying on hierarchical control structures and dynamical optimization will lead to an increase in vehicle density and speed. Using graph theory, one can investigate how a group of autonomous vehicles (like mobile robots) can solve a formation problem at intersection. The student should be familiar with MATLAB. Knowledge on graphs is also appreciated but not necessary.
MER: Denis Gillet
Type of project: Master
Contact: Laleh Makarem
Decentralized Control of Autonomous Vehicles on Pre-defined Paths using Decentralized Navigation Functions
Electric Automated Guided Vehicles (AGVs) operating on roads have a high potential for reducing CO2 emissions and traffic congestion in inter modality areas. Advanced maneuvering solutions relying on hierarchical control structures and dynamical optimization will lead to an increase in vehicle density and speed.
In this project, preliminary work on optimal crossing using decentralized navigation functions will be extended to take into account vehicles dynamics and energy constraints during coordinated maneuvers. Typically, priority will be given to heavy vehicles with little energy reserve.
MER: Denis Gillet
Type of project: Master or Semester
Contact: Laleh Makarem
Global Convergence in Cooperative Coordination of Autonomous Vehicles at Intersections
The coordination of autonomous vehicles is of particular importance to multi-robot navigation. In our group, a method of coordination has been developed which uses decentralized navigation functions.
In this project, the goal is to study the proposed navigation function and investigate its theoretical properties with respect to convergence and stability.
MER: Denis Gillet
Type of project: Master or Semester
Contact: Laleh Makarem