Learning for decision and control in a population of dynamical systems

In real-world applications there are often many individual systems with similar dynamics - a population of systems. For example batteries in different cars, different batches in the process industry or different patients in medicine.

By taking a population view we can study how the dynamical systems in the population can learn from each other, both when it comes to estimating models and designing controllers, and how to detect systems with anomalous behavior that may need maintenance or replacement. The aim of this project is to develop methods and analysis to tackle these problems in a coherent way.

To realize this we have identified three subprojects.

In the first we will develop and analyze estimation methods for populations of systems that can also handle anomalous systems. In the second we will study how these models can be used for population-wide monitoring and anomaly detection. In the third we will develop methods for robust data-driven control, where individual systems learn from each other.

We already have established collaborations with experts in industry that can provide us with relevant applications and data for evaluation of the developed methods.By considering populations, instead of single systems as traditionally done in control theory, and taking on these three problems in a coherent way we believe that this project will open up new research directions in, and make significant contributions to, data-driven modeling, fault detection and control.