Goal of the Workshop
This workshop celebrates the 60th birthday of Professor Andrew R. Teel, whose groundbreaking contributions have had a profound impact on the field of control theory, particularly in the areas of hybrid systems, nonlinear control, and stability theory. The workshop will bring together lead- ing researchers who have been influenced or inspired by Teel’s work. Each speaker will present state-of-the-art results that connect to themes he has developed or championed, including hybrid systems, robust and optimal control, multi-agent dynamics, and control with limited information.
Organizers
• Rafal Goebel, Loyola University
• Jorge Poveda, UC San Diego
• Ricardo Sanfelice, UC Santa Cruz
List of Speakers
• Jorge Poveda, UC San Diego
• Hyungbo Shim, Seoul National University
• Alessandro Astolfi, Imperial College London
• Daniel Liberzon, University of Illinois Urbana-Champaign • Joao Hespanha, UC Santa Barbara
• Dragan Nesic, University of Melbourne
• Luca Zaccarian, LAAS-CNRS and University of Trento
• Ricardo Sanfelice, UC Santa Cruz
Talk Abstracts
Prescribed-Time and Fixed-Time Stability in Hybrid Dynamic Inclusions, by Jorge Poveda
This talk discusses recent results on the design and analysis of hybrid feedback laws for achieving fixed-time and prescribed-time stability properties. We study general classes of hybrid dynamical systems, including constrained and time-varying nonlinear systems, and show how Lyapunov and converse Lyapunov techniques can be used to achieve arbitrarily fast convergence bounds. Several connections with robust control and homogeneity theory will be presented.
Emergence, Robustness, and Adaptation in Heterogeneous Multi-Agent Systems, by Hyungbo Shim
Multi-agent systems exhibit rich collective behaviors, such as flocking, synchronization, and emergence of leaders. This talk introduces our recent study on the emergence of homoge- neous input-output response from a group of heterogeneous agents with a diffusively coupled feedback law. In the emergent behavior, all agents are approximately described by a single input-output map. Robustness to perturbation and adaptation to improve tracking are dis- cussed with several examples of physical systems, such as vehicle formations, synchronous generators, and biochemical reaction networks.
Steady-State Optimal Filtering for Linear and Nonlinear Systems, by Alessandro Astolfi
This talk focuses on optimal filtering problems for nonlinear and linear systems in contin- uous time. By leveraging ideas from Lyapunov stability theory, invariant manifolds, and hybrid systems, the talk presents results that, departing from the celebrated Kalman filter, allow for the derivation of steady-state filters for general systems with unknown inputs and measurement noise. The approach hinges on the geometric separation of ”slow” and ”fast” dynamics and extends to a class of hybrid filtering structures.
Localization and Mapping with Coarse Information, by Daniel Liberzon
This talk explores problems of robot localization and mapping in settings where only coarse, low-resolution information is available?for example, in the form of proximity relations or qualitative constraints. We present a general framework for modeling such problems using set membership and hybrid systems approaches, and develop new algorithms for solving them using control-theoretic ideas. Applications to GPS-denied environments and low-cost sensors are discussed.
Learning with Contextual Information in Non-Stationary Environments, by Joao Hespanha
In many learning problems, the data distribution may change over time and may depend on hidden variables, or ”contexts,” that evolve on a different timescale than the primary task. This talk presents recent advances in contextual bandits and reinforcement learning for such non-stationary environments, with connections to system identification and adaptive control. Methods to detect and exploit context changes are proposed and their performance is analyzed theoretically and empirically.
Stability of Optimal and Near-Optimal Control Laws, by Dragan Nesic
This talk revisits the classical problem of optimal control from a stability perspective. We focus on discrete-time nonlinear systems and study the stability of optimal and near-optimal control laws, including model predictive controllers. New Lyapunov-based criteria are pro- posed to guarantee asymptotic stability and performance bounds for approximate solutions to the Bellman equation. Applications to constrained control and data-driven optimization are also considered.
The Model Recovery Anti-Windup Paradigm: An Essential Winding Roadmap, by Lucca Zaccarian
Since the introduction of the Model Recovery Anti-Windup (MRAW) approach, a number of results have been derived for LTI systems and more recently extended to nonlinear systems and switched strategies. In this talk, we revisit the fundamentals of MRAW and clarify its core properties, such as recovery of the unconstrained closed-loop dynamics when saturation is absent. We highlight key applications where the method has led to improved performance and robustness, and outline ongoing research directions.
Feasibility and Regularity of Barrier and Lyapunov-Based Controllers for Dynamical Systems, by Ricardo Sanfelice
This talk presents recent advances in converse theorems for safety and stabilization using bar- rier and Lyapunov functions, with an emphasis on their feasibility and regularity properties. For continuous-time systems modeled as differential inclusions, we show via counterexam- ples that autonomous and continuous barrier functions may fail to exist even for smooth and safe systems. Motivated by converse Lyapunov theorems, we establish the necessity and suf- ficiency of time-varying barrier functions under mild assumptions, constructing such func- tions using marginal reachability-based formulations and nonsmooth analysis. We further demonstrate that these constructions inherit regularity from the system and, in the smooth case, imply the existence of smooth barrier certificates. In the hybrid setting, we examine the existence of smooth control Lyapunov functions for asymptotically stabilizable compact sets, and derive conditions under which continuous state-feedback laws exist. Finally, we propose minimum-norm controllers for hybrid systems by selecting inputs that minimally decrease Lyapunov functions across flows and jumps. These results align with the work- shop’s focus on optimization-based controllers, providing foundational insights into when such controllers exist and how their regularity can be guaranteed.