Hybrid Control and Estimation of Semi-Dissipative Systems- Analysis, Computation, and Machine Learning
Abstract
The proposed research is aimed at developing a rigorous theoretical and computational framework for hybrid control of semi-dissipative systems, combining applied mathematics and machine learning (ML) techniques. This topic is motivated by control and optimization of fluid transport and mixing problems. Understanding mass transport, fluid mixing, and their asymptotic behaviors via active control of the flow advection leads to fundamental, yet highly challenging problems often found in industrial and engineering applications. Applications include, but are certainly not limited to, ventilation in energy efficient buildings, mixing for bioorganic nutrient conversion, and activated sludge systems in industrial wastewater treatment. The goal of this project is to place these problems within a flexible and rigorous theoretical and computational framework, and to develop solution strategies utilizing hybrid dynamical control and estimation, optimization, and machine learning (ML) tools. Of special note is the hybrid nature of the controls which involve integration of continuous- and discrete-time dynamics in their design. The proposed control designs, when combined with the corresponding learning and sampled information will naturally lead to hybrid control laws, enabling the data-driven updating and adaptive computation for real-time control and estimation. These problems are rich and challenging, both in theory and their computational aspects, and open a new universe of high-potential research opportunities. They also provide inspirational ingredients needed to build the systematic research agenda, aiming to contribute to the development of new control-inspired efficient methods for ML and a new body of theoretical and computational control methods.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 14, 2024
- Source ID
- FA95502310675
Entities
People
- Weiwei Hu
Organizations
- Air Force Office of Scientific Research
- The University of Georgia
- United States Air Force