Tuesday, March 1, 2022 10am to 11:15am
About this Event
Recent technological advances have made devices for actuation, sensing, computation, and communication increasingly portable, inexpensive, and prevalent in societal engineering network systems. Examples include connected autonomous vehicles, robotic networks, the power grid -and its emergent energy markets-, and intelligent transportation systems. The emerging use of purely data-driven mechanisms to control and optimize in real time these complex network systems has led to the awareness of the pitfalls of model-free decision making without stability and robustness guarantees. This limitation is further exacerbated by the complex interactions that emerge between continuous-time and discrete-time dynamics in closed-loop systems, which difficult the development of rigorous stability, convergence, and robustness certificates via control theoretic tools. To address these challenges, in this talk I will present some of our recent advances in the context of feedback control with data-assisted feedback loops, with a focus on nonsmooth and hybrid control approaches. The proposed controllers are suitable for the solution of model-free control, optimization, and coordination problems in complex dynamical systems subject to topological constraints, high-performance requirements, and safety demands. The algorithms exploit non-Lipschitz and hybrid (continuous and discrete) dynamics to overcome fundamental limitations of standard smooth adaptive algorithms, achieving accelerated model-free control without sacrificing critical stability and robustness guarantees. Extensions to model-free time-varying decision-making problems in game theoretic settings will also be discussed in the context of coordinated network games. Applications in robotic networks, transportation systems, and energy systems will be presented to illustrate the main results.