MC 6460 and Zoom (Please contact firstname.lastname@example.org for the meeting link)
Melkior Ornik, University of Illinois Urbana-Champaign
Resilience and Guaranteed Task Completion for Partially Unknown Nonlinear Control Systems
The ability of a system to correctly respond to a sudden adverse event is critical for high-level autonomy in complex, changing, or remote environments. By assuming continuing structural knowledge about the system, classical methods of adaptive or robust control largely attempt to design control laws which enable the system to complete its original task even after an adverse event. However, catastrophic events such as physical system damage may render the original task impossible to complete. In other words, any control law that attempts to complete the task is doomed to be unsuccessful. Thus, an autonomous planner should recognize the task as impossible to complete, propose an alternative that can be completed given the current knowledge, and formulate a control law that drives the system to complete this new task. To do so, we propose a twin framework of resilience and guaranteed performance. Combining methods of optimal control, online learning, and reachability analysis, our approach offers an ability to (i) compute a set of tasks completable under all system dynamics consistent with the planner’s partial knowledge and (ii) use online learning methods to design an appropriate control law. By briefly presenting several applications of the developed theory, I will also identify promising future directions of research, including guaranteed reachability for systems evolving on manifolds, output-based certifiable task assignment, and resilience of systems with time delays.