Safe Stabilization and Tracking for Dynamical Systems under Model Uncertainty

Nikolay A. Atanasov

Assistant Professor, Department of Electrical and Computer Engineering at University of California, San Diego

Seminar Information

Seminar Series
Dynamic Systems & Controls

Seminar Date - Time
February 10, 2023, 3:00 pm
-
4 PM

Seminar Location
EBUII 479, Von Karman-Penner Seminar Room


Abstract

Automatic control systems are increasingly deployed in unstructured environments featuring novel and dynamically changing conditions. Ensuring successful operation in such scenarios requires revisiting the core problems of stabilization and tracking to impose strict safety constraints. In the context of autonomous mobile robot navigation, this talk will review control Lyapunov function (CLF) and control barrier function (CBF) techniques for joint stabilization and safety, and will present several extensions to enable safe output tracking under uncertainty in the system dynamics and safety constraints. To track a desired output trajectory safely, we will introduce a virtual reference governor system to act as an adaptive regulation point for the original system. The governor speeds up along the output trajectory when safety is not endangered and slows down otherwise. Operation in novel environments also means that any prior system dynamics model may be inaccurate and that safety constraints may be imposed in real time in response to sensor measurements. We will discuss identifying system dynamics from past trajectory data, constructing safety constraints from sensor measurements, and how these translate to probabilistic and distributionally robust CLF and CBF constraints.
 

Speaker Bio

Nikolay Atanasov is an Assistant Professor of Electrical and Computer Engineering at the University of California San Diego, La Jolla, CA, USA. He obtained a B.S. degree in Electrical Engineering from Trinity College, Hartford, CT, USA in 2008, and M.S. and Ph.D. degrees in Electrical and Systems Engineering from University of Pennsylvania, Philadelphia, PA, USA in 2012 and 2015, respectively. His research focuses on robotics, control theory, and machine learning with applications to active perception problems for autonomous mobile robots. He works on probabilistic models that unify geometric and semantic information in simultaneous localization and mapping (SLAM) and on optimal control and reinforcement learning algorithms for synthesizing robot control policies that minimize uncertainty in these probabilistic models. Dr. Atanasov's work has been recognized by the Joseph and Rosaline Wolf award for the best Ph.D. dissertation in Electrical and Systems Engineering at the University of Pennsylvania in 2015, the best conference paper award at the IEEE International Conference on Robotics and Automation (ICRA) in 2017, and the NSF CAREER award in 2021.