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.
A standard approach to multi-robot systems is to divide the team-level tasks into suitable building blocks and have the robots solve their respective subtasks in a coordinated manner. However, by bringing together robots of different types, it should be possible to arrive at completely new capabilities and skill-sets. In other words, the whole could become greater than the sum of its parts.
The dynamics of the atmospheric boundary layer (ABL) play a fundamental role in wind farm power production, governing the velocity field that enters the farm as well as the turbulent mixing that regenerates energy for extraction at downstream rows.
Graphon has recently been introduced by Lovasz, Sos, etc. to study very large graphs. A graphon can be understood as either the limit object of a convergent sequence of graphs, or, a statistical model from which to sample large random graphs. We take here the latter point of view and address the following problem: What is the probability that a random graph sampled from a graphon has a Hamiltonian decomposition?
This presentation considers the general problem of estimation of the position and the attitude of rigid bodies for indoor applications, when accurate GPS signals are not available. To solve this observation problem, magneto-inertial measures are exploited and physics-based filters are designed. Real experiments are provided to illustrate the performance and the lack of observability for some indoor applications.
This talk presents two recent PDE control applications using backstepping. The first part focuses on improving state-of-charge (SoC) and state-of-health (SoH) estimation in lithium-ion batteries, which are essential for electrified transportation and energy storage.
In this talk, I will present material related to flight control definitions, application, technical challenges and open problems. Recent advances in robust and adaptive flight simulation and control technologies will be discussed.
Autonomous systems have undergone significant changes over the past five-ten years thanks to technological advancements that have been leveraged to meet a diverse set of interaction requirements driven by performance and capability needs. Conventional control strategies were typically designed for robustness and speed of the automated system within a controlled and well-regulated environment.
Strong stabilization of a plant is defined as finding a stabilizing feedback controller that is itself stable. This problem has attracted interest since the 1970s, and various design techniques have been proposed. On the other hand, finding the H∞ optimal (robustly optimal) controller in the set of all stable stabilizing controllers is still an open problem. A suboptimal solution can be found under certain sufficient conditions.
The framework of multi-agent learning explores the dynamics of how individual agent strategies evolve in response to the evolving strategies of other agents. Of particular interest is whether agent strategies converge to well-known solution concepts such as Nash Equilibrium (NE). Most “fixed order” learning dynamics restrict an agent’s underlying state to be its own strategy. In “higher order” learning, agent dynamics can include auxiliary states that can capture phenomena such as path dependencies.
We address the challenge of promoting sustainable practices in production forests managed by strategic entities that harvest agricultural commodities under concession agreements. These entities engage in activities that either follow sustainable production practices or expand into protected forests for agricultural growth, which leads to unsustainable production. Our study uses a network game model to design optimal pricing policies that incentivize sustainability and discourage environmentally harmful practices.
