CAREER: An Efficient Computational Framework for Data Driven Feedback Control

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Optimal control is a mathematical challenge that aims to find control actions that guide a controlled system to achieve optimal-cost performance. Most existing methods for optimal control assume that the state of the controlled system is fully observed, and therefore that the feedback from the controlled state is explicitly available. In many practical cases, however, the controlled system is not directly observable. In this project, the investigator aims to develop efficient and accurate data assimilation methods to analyze indirect observational data and to construct an efficient computational framework to design optimal control based on the information contained in the observational data. This new framework has the potential to benefit the control and design materials at the nano-scale as well as the controls of power systems at various scales to support reliable and an efficient electric grid. The project incorporates activities to train the next generation of data scientists with both mathematical insight and technical skills to address important practical challenges.The goal of this project is to develop an efficient computational framework that will allow data to optimally drive control actions. The primary efforts will be dedicated to the development of data driven optimal control methods, incorporating state-of-the-art data assimilation methods into stochastic optimal control solvers. The research carried out in this project also aims to elucidate inherent connections between data and actions. The research has two thrusts. The first thrust will focus on the development of mathematical and computational methods to establish an efficient data driven feedback control framework. Then, the methods developed in the first thrust will be applied to solve practical scientific and engineering feedback control problems in the second thrust.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.