Back-to-Basics Tutorials

Back-to-Basics Tutorial I:

Abstract: What kind of research might a control student do in 2026 and onward? For example, any kind that leverages BOTH the capabilities of AI/Machine Learning AND the rigorous, abundant heritage of the control canon. In particular, compared to the powerful late-1980s-and-later control tools for nonlinear, robust or stochastic, adaptive, and infinite-dimensional systems - the RL/ML-only approaches may demand little effort or training. But won't reward with insight.

One subject in which the future and the past intertwine is PDE control. (i) Control laws complex enough to demand neural models for implementation. (ii) Elegant. (iii) Of unequalled connection to physics (fluids, thermal, electrochemistry, materials/manufacturing) and even bio-medical and social sciences.

This BTBT session introduces the basics of (1) PDE backstepping design and (2) neural operator methodologies for implementing such controllers, while retaining theoretical guarantees.

Learning in control's early decades was ONLINE - "adaptive." Learning by ML has meant OFFLINE - of static maps: feedback laws, Lyapunov functions, CBFs. Come - and learn - combined offline+online learning through “adaptive PDE backstepping” control.

Biography: Miroslav Krstic is a distinguished professor and just completed 14 years of service in a vice chancellor for research role at UC San Diego, to take on the duty of editor-in-chief of IEEE Transactions on Automatic Control. He is the recipient of the IEEE Brockett Award and Bode Prize, ASME Oldenburger Medal, SIAM Reid Prize, Bellman Award, and other recognitions, including the IFAC Chestnut prize and the IFAC TC awards for nonlinear, adaptive, distributed-parameter, and delay systems. He is a Fellow-Ambassador of the French CNRS, member of the Serbian Academy of Sciences and Arts, and fellow of IEEE, IFAC, SIAM, ASME, AIAA, and other societies. He is a coauthor of 19 books and several hundred papers on various nonlinear, adaptive, and infinite-dimensional control subjects.

Back-to-Basics Tutorial II:

Abstract: The basic consensus dynamics will be presented for reaching an agreement in static graphs including DeGroot opinion dynamic and Cai-Ishii (push-pull) dynamic. The dynamic will be interpreted through an optimization point of view to gain a unifying ground for viewing and studying such dynamics. These protocols will be used as basic mechanisms to coordinate decentralized computations among agents, connected over a network, through local message exchange. Then, simple gradient-based methods will be discussed with basic intuition of how and why one would expect the methods to converge, and how fast they can converge.

Biography: Angelia Nedich has a Ph.D. from Moscow State University, Moscow, Russia, in Computational Mathematics and Mathematical Physics (1994), and a Ph.D. from Massachusetts Institute of Technology, Cambridge, USA in Electrical and Computer Science Engineering (2002). She has worked as a senior engineer in BAE Systems North America, Advanced Information Technology Division at Burlington, MA. Currently, she is a faculty member of the school of Electrical, Computer and Energy Engineering at Arizona State University at Tempe. Prior to joining Arizona State University, she has been a Willard Scholar faculty member at the University of Illinois at Urbana-Champaign, Urbana, IL. She is a recipient (jointly with her co-authors) of the Best Paper Award at the Winter Simulation Conference 2013 and the Best Paper Award at the International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt) 2015. Her general research interest is in optimization, large scale complex systems dynamics, variational inequalities and games.

Back-to-Basics Tutorial III:

Abstract: Hybrid dynamical systems provide a powerful framework for modeling and analyzing systems that exhibit both continuous and jumping behavior, with broad applications in control. This session introduces a now-standard approach based on the specification of flow and jump dynamics via flow sets, flow maps, jump sets, and jump maps. It presents a solution concept grounded in set-valued analysis that enables rigorous treatment of existence, stability, and robustness. Under mild structural conditions on the system data, powerful tools from classical dynamical systems and control theory extend naturally to the hybrid setting, including Lyapunov methods, invariance principles, and perturbation techniques such as singular perturbations, averaging, and small-gain arguments. Selected results will be developed alongside illustrative examples that highlight both modeling practice and analysis. The goal of the session is to equip participants with a working understanding of the framework and its analytical tools, enabling them to model hybrid systems and apply modern stability and robustness techniques in their own work.

Biography: Andrew R. Teel received his A.B. degree in Engineering Sciences from Dartmouth College in Hanover, New Hampshire, in 1987, and his M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Berkeley, in 1989 and 1992, respectively. After receiving his Ph.D., he was a postdoctoral fellow at the Ecole des Mines de Paris in Fontainebleau, France. In 1992 he joined the faculty of the Electrical Engineering Department at the University of Minnesota, where he was an assistant professor until 1997. Subsequently, he joined the faculty of the Electrical and Computer Engineering Department at the University of California, Santa Barbara, where he is currently a Distinguished Professor and Director of the Center for Control, Dynamical systems, and Computation. His research interests are in nonlinear and hybrid dynamical systems, with a focus on stability analysis and control design. He has received NSF Research Initiation and CAREER Awards, the 1998 IEEE Leon K. Kirchmayer Prize Paper Award, the 1998 George S. Axelby Outstanding Paper Award, and was the recipient of the first SIAM Control and Systems Theory Prize in 1998. He was the recipient of the 1999 Donald P. Eckman Award and the 2001 O. Hugo Schuck Best Paper Award, both given by the American Automatic Control Council, and also received the 2010 IEEE Control Systems Magazine Outstanding Paper Award. In 2016, he received the Certificate of Excellent Achievements from the IFAC Technical Committee on Nonlinear Control Systems. In 2020, he and his co-authors received the HSCC Test-of-Time Award. He served as Editor-in-Chief for Automatica from 2017 through 2025. He is a Fellow of the IEEE and of IFAC.