The goal of this workshop is to present novel results in the area of deterministic and stochastic hybrid systems applied to robust optimization and learning in dynamical systems. These include, but are not limited to, hybrid extremum seeking control, event-triggered sampled-data optimization, stochastic learning in asynchronous sampleddata games, robust set-based estimation, and event-triggered Q-learning optimal control. The workshop will present constructive approaches for the design of novel robust hybrid algorithms for black-box and grey-box optimization, as well as some engineering applications that motivate the study of hybrid optimization and learning. After finishing the workshop attendees will be familiar with a broad class of hybrid dynamics in the area of black-box and grey-box optimization, control, and estimation, as well as a basic understanding on how to design and analyze some of these hybrid algorithms. The merits of working with hybrid dynamics will be clearly illustrated throughout the workshop.
Target audience: The workshop is intended to be a brief course on recent analysis and design tools for robust algorithms for modelfree optimization and learning using deterministic and stochastic hybrid dynamics. It targets a broad audience in academia and industry, including graduate students looking for an introduction to a new and active area of research; control practitioners interested in novel design techniques and applications; and researchers in dynamical systems, optimization, and control. The workshop audience is not expected to have any advanced background in hybrid systems or optimization. A basic knowledge of linear/nonlinear system theory and probability theory is useful.