CAREER: A Universal Framework for Safety-Aware Data-Driven Control and Estimation

University Of Vermont & State Agricultural College, Burlington VT

Investigators

Abstract

This Faculty Early Career Development Program (CAREER) award supports research that enables safe data-driven control and estimation methods for robotics and power systems, thereby promoting the progress of science, advancing prosperity and welfare, and securing the national defense. This research will develop a universal framework for the simultaneous design of control policies and safety measures based on recent advances in the mathematical modeling of dynamical systems. In this context, the intersection between safety, control systems, and data-driven methodologies is still nascent and there are major hurdles to overcome for the adoption and acceptance of the safe data-driven paradigm despite the immense progress in data processing capabilities. This project will solve these challenges through the automatic synthesis of safety-aware control laws for highly complex systems as a function of their real-time input-output information. The outcomes of this work will be well-suited for a variety of applications in engineering, biology, and manufacturing that are of essential significance for the economic development and the competitiveness of the nation on the global stage. The project’s research objectives are complemented by a methodical industry outreach and pedagogical plan aimed at blending research and education, strengthening industry collaboration, and boosting the participation of underrepresented communities for the benefit of society at large. The researched framework leverages the algebraic structure of a novel framework for system representation that systematically encodes its input-output behavior, namely the Chen-Fliess framework. This encoding naturally fits the underpinnings of the data-driven paradigm for control and estimation. This methodology essentially enables the use of algebraic optimization routines on the system’s information, by eliminating the need for a state-space coordinate frame that otherwise will require over-parametrizations that can lead to infeasibility. Consequently, faster and less power-consuming optimization algorithms are researched with the capability of retaining the system’s input-output behavior. Thus, reachability analysis and synthesis of the control barrier functions will then be performed under this algebraic framework to advance these methodologies in the data-driven setting. The specific objectives of this project are to (1) provide an algebraic framework for the analysis and optimization of data-driven control systems, (2) develop input-output reachability analysis in the Chen-Fliess framework, and (3) develop data-driven methods for the synthesis of safe control laws based on reachability analysis and control barrier functions in the Chen-Fliess framework. The researched work will be validated on autonomous vehicles, a multi-robot system performing simultaneous localization and mapping, and a data-driven power system regulation problem. 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.