The project is associated with the analysis and control of large scale networks, and involves the development of corresponding theoretical tools as well as the study of related applications associated with engineering and biological networks.
In particular, for the case of engineering networks our research has focused on the design of decentralized control policies with a plug-and-play capability, i.e. stability guarantees are provided for the network when heterogeneous subsystems are added or removed from the network. This is a major requirement in many important applications such as smart grids and power networks, where the increasing presence for distributed generation, fluctuations from renewable energy sources and their low inertia, and the introduction of load-side participation schemes impose severe challenges associated with the ability to ensure a stable network with good performance. Throughout the project various results have been developed that lead to appropriate decentralized design protocols for such networks. These improve their efficiency and reliability, and facilitate the implementation of such highly distributed control schemes.
For the case of biological networks our research has focused on the analysis of the effects of noise in biochemical reaction networks at the molecular level. Such molecular fluctuations can drive metabolites away from desired concentrations or be sometimes advantageous contributing to diversity and evolution. Furthermore, such stochastic models are also relevant in epidemiological models and can be used to understand the role of feedback when mitigation strategies are introduced. Throughout the project novel tools have been developed for analyzing the stochasticity in classes of biochemical reactions, and tools for constructing optimal feedback policies in stochastic epidemiological models have been developed where various generic properties of those have been characterized.
