The Dynamic Systems and Control group at UC San Diego integrates, at a fundamental level, system design, modeling, and control disciplines to obtain improved performance of the dynamic response of engineering systems using feedback. As such, the areas of research of the Dynamic Systems and Control group is a joint activity in the topics of systems integration, dynamic system modeling, feedback control design, and the fundamentals of systems theory as applied to linear and nonlinear dynamic systems, mechatronics, structural control, aerospace, and fluid-mechanical systems.
This talk will review a fundamental building block of dynamic game theory—the linear-quadratic game—and discuss how Nash equilibrium solutions differ as a consequence of the information players have access to at different times. In this context, we will examine several recent results, aligned to the following questions:
(i) How can we find feedback strategies which closely approximate Nash solutions, but minimize inter-agent communication/sensing?
All life and tech that has evolved since bacteria has a Universal Layered Architecture (ULA) driven by the need for robustness in individuals, groups, and lineages (i.e. evolution).
There has been tremendous advancements in high performance robots,
drones, and other autonomous or semi-autonomous systems. At the same
time, interest is growing in use of large networks of (relatively)
inexpensive agents for a variety of tasks. This often implies
limitations on power (actuation), bandwidth (communication frequency),
homogeneity of agents, or configuration reliability (loss or addition of
some agents during operation). Strong results exist for most of these
challenges at a single agent level and they all result some level of
Moment matching is a model reduction technique which allows constructing reduced-order models preserving specific moments. These moments are associated with the steady-state output response of the system to be reduced interconnected in an open-loop fashion to a signal generator. Model matching thus relies on strong stability properties and on the availability of a model of the signal generator.
How should one optimally allocate limited resources when facing strategic competitors with imperfect information? This fundamental question underlies many scenarios, from protecting critical infrastructure to deploying cybersecurity resources. We examine this through extensions of the Colonel Blotto game - a classical model where two players compete by distributing limited resources across multiple battlefields. While traditional analysis assumes perfect information and independent battlefield valuations, modern applications demand more nuanced models.
Thermodynamics was conceived in the 19th century to quantify the efficiency of steam engines, but its principles have since permeated disciplines ranging from chemistry and biology to astrophysics and the study of the quantum world. While classical thermodynamics describes idealized, quasi-static transitions, real engines cycle through finite time and the power—not just their efficiency—matters. Progress in this regime has been hindered by the complexities of non-equilibrium dynamics.
Systems companies struggle to integrate into complex designs components coming from various providers. The news of recalls and re-certifications in the automotive and aerospace industries is an eloquent testimony to the difficulty of system design.
Machine learning algorithms are increasingly being deployed into environments in which they must interact with other strategic agents like algorithms and people with potentially misaligned objectives.
When designing complex systems, we need to consider multiple trade-offs at various abstraction levels and scales, and choices of single components need to be studied jointly. For instance, the design of future mobility solutions (e.g., autonomous vehicles, micromobility) and the design of the mobility systems they enable are closely coupled.
Autonomous agents, whether on the ground or in the air, must navigate shared spaces, avoiding obstacles or coordinating within a group. Effective navigation in dynamic environments requires adaptive methods that ensure safety while achieving the agents' goals. In this talk I will present two of our latest results in multi-agent navigation: AVOCADO and Gen-Swarms.
