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.
The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, the translation of these successes to the domain of dynamical physical systems remains a significant challenge. Closing the loop from data to actions in these systems faces many difficulties, stemming from the need for sample efficiency and computational feasibility, along with many other requirement such as verifiability, robustness, and safety.
In many real-world settings, image observations of physical systems, such as satellites, may be available when low-dimensional measurements are not.
We live in an increasingly electrified world.
This seminar will provide an overview of the programs within the Dynamics, Controls, and Cognition (DCC) Cluster, namely the Dynamic, Control, and Systems Diagnostics (DCSD), the Mind, Machine, and Motor Nexus (M3X), and the Foundational Research in Robotics (FRR).
In this talk I will try to survey developments in automotive computer control systems during the past four decades, which incidentally and fortuitously coincided with my career in this exciting and challenging field. Starting in the late 70s and early 80s with just dozens or at most hundreds of lines of asse
The dynamics of a wide variety of vehicles can be represented using noncanonical Hamiltonian system models with dissipation and exogenous inputs.
The past year and a half has been marked with an increasing cadence of exciting space missions by both nation states and private actors toward the Moon and greater cislunar domain. With the manifest for cislunar missions only increasing in the yea
Multi-agent deployment for coverage is a classical problem that appears in many applications such as area surveillance and monitoring, wireless communication coverage, or data harvesting. The Voronoi partitioning method and its variations have long been recognized as the leading solution to this problem.
Perhaps one of the most important uses of optimal or robust control design is for characterizing plants rather than just designing optimal controllers.
