Control design based on deep learning

Abstract:  Innovation in deep learning methods, tools, and technology offers an unprecedented opportunity to transform the control engineering practice and bring much excitement to control systems theory research. In this talk, I will introduce recent results in modeling dynamic systems with deep learning representations that embed domain knowledge. I will also discuss differentiable predictive control, a data-driven approach that uses physics-informed deep learning representations to synthesize predictive control policies. I’ll illustrate the concepts with examples from engineering applications.

Bio:  Draguna Vrabie is a Chief Data Scientist in the Data Sciences and Machine Intelligence Group at Pacific Northwest National Laboratory. She serves as Team Lead for the Autonomous Learning and Reasoning Team and as Thrust Lead for PNNL’s Data Model Convergence Initiative. Her work focuses on design and optimization of autonomous systems by bringing together scientific machine learning with theoretical methods for decision and control. Her research bridges theory, computational methods, and practical application to energy and critical infrastructure systems. Vrabie leads PNNL’s contributions the ECP ExaLearn investment on scalable reinforcement learning, and PNNL’s project portfolio on applied ML for modeling, simulation and control for energy efficiency building systems in support of EERE’s Building Technology Office. Vrabie co-authored two books on optimal control, reinforcement learning, and differential games and has published over seventy peer-reviewed journal and conference papers. Her work was recognized with Best Paper awards and two corporate awards for outstanding achievement and operational excellence. Vrabie holds seven patents, and in 2021 she received an R&D100 Award. Before joining PNNL in 2015, she was a senior scientist in the Control Systems Group at United Technologies Research Center, East Hartford, Connecticut, from 2010-2015. Vrabie received a Ph.D. in Electrical Engineering from the University of Texas at Arlington for her work on adaptive optimal control by reinforcement learning principles. She received Dipl. Ing. and M.S. degrees in Automatic Control and Computer Engineering from the Gheorghe Asachi Technical University in Iasi, Romania.

***Event will take place in hybrid format. The location for in-person attendance will be room 1311 EECS.   Attendance will also be possible via Zoom. Zoom link and password will be distributed to the Controls Group e-mail list-serv.

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