Computational Control

These on the left are eight control problems. The tasks are to design

1. a thermostatic valve ensure proper temperature regulation in an engine

2. an update rule for the interest rate at a central bank to control inflation

3. a cruise controller to ensure safe and fuel-efficient platooning

4. a congestion management protocol in data networks

5. the logic of an artificial pancreas for diabetic patients

6. the controller in electric power plants to ensure a constant grid frequency

7. a controller to land a space rocket

8. an audio amplifier that ensures constant gain over a large range of frequencies.

You can check the first deck of slides (Introduction) for more details.

If you have taken an introductory Control Systems course, you should be able to work on problems 1, 6, and 8 (although fewer and fewer EE students know how to draw an electronic circuit as a feedback system!).

Problems 2 and 4 require different competences. Generally speaking, 2 requires at least some notions of Game Theory, 4 requires some notions of Distributed Systems.

At the end of the Computational Control course, you'll be able to work on problems 3, 5, 7 (and similar ones). These are control problems in which model uncertainty, stringent constraints, and complex dynamics call for advanced control solutions.
We will tackle them via cyber-physical solutions, that is, closing the loop on these plants via embedded computers. The resulting controllers will have a significant computational component, hence the name of the course.

You are a control expert, and you are asked to act as a consultant for InsulinCo, a company that provides artificial pancreas (AP) care to patients with diabetes. Their AP uses an insulin pump to inject insulin in response to glucose measurements and meal predictions. InsulinCo would like to know if an advanced control method can improve the performance of their AP.

To test/validate the controllers you develop, InsulinCo has provided you with the ReplayBg simulator, which simulates the nonlinear dynamics of a human glucose regulation mechanism.

To support the data-driven approach, InsulinCo has provided the time series data for a single sample patient that they would like you to use for the data-driven controller.

You need to prepare a 5-slide presentation and a Jupyter notebook for the InsulinCo Chief Technology Office, explaining what model-based control strategy you recommend, showing how data could be used to develop a model-free control strategy, and convincing them to give you access to more data to demonstrate it.

Thanks to Simone del Favero and Elena Idi from the Bioengineering Group of the University of Padova (Italy) for providing the students with the data and the simulator and for their guest lecture during the course!