Distributed control architecture synthesis

Control of integrated networks is challenging due to strong interactions between variables (limiting performance of decentralized controllers) and large system size (difficult design of a centralized controller). In this context, distributed controllers pose an optimal architecture with reduced system size and inclusion of key interactions. A key question is how to decompose an integrated system into distributed architecture. We address this problem via structural analysis. Specifically, we abstract the control system into an equivalent graph. Subsequently a graph partition algorithm is used to decompose the graph into subgraphs with desired partition objective. Each of these subgraphs correspond to a distributed subcontroller. The key challenge lies in connecting the decomposition objective with the equivalent graph and partition objective. In this project, we will explore establishing connection between new decomposition objectives and the corresponding graph decomposition methodology. Additionally, we will focus on pursuing data-based analysis using concepts of artificial intelligence (AI) and machine learning (ML). 

Key skills required/expected: Matlab (for simulation), Advanced control, basics of AI/ML.