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.
In this talk, I will take you on two brief journeys that have fascinated me, personally, for a few years, now. The first journey will focus on the lithium-sulfur (Li-S) battery chemistry, which promises to achieve revolutionary specific energy lev
This article provides a protocol to address the robust output feedback consensus problem for networked heterogeneous nonlinear negative-imaginary (NI) systems with free body dynamics.
Autonomous systems often need to interact with humans. Explaining and predicting human decisions in these interactions is critical for the safe and efficient operations of autonomous systems.
The desire to optimize performance is pervasive across engineering disciplines - and society for that matter – and the advent of ‘big data’ and learning-based methods have brought leaps in performance in many areas.
In this talk, I will build the connection between Hamilton-Jacobi-Bellman equations (HJB) and the multi-armed bandit (MAB) problems. HJB is an important equation in solving stochastic optimal control problems.
Many approaches to machine learning have struggled with applications that possess complex process dynamics. In contrast, human intelligence is adapted, and - - arguably - built to deal with complex dynamics.
We consider the used-in-practice setting of actor-critic where proportional step-sizes are used for both the actor and the critic, with only one critic update with a single sample from the stationary distribution per actor step.
Deploying massive swarms of robots to solve real-world problems has been a research promise for over 50 years, but even today we still do not see clear paths for how an engineer should design, deploy, and control one million robots simultaneously.
We are in times that witness the dawn of a new science: the science of automated vehicle traffic. Although fully automated vehicles are still rare in our roads, we are in a position to find and predict the laws and equations of this new science. The foundations of the new science are built with the help of many areas in mathematics, like Theory of Partial Differential Equations, Numerical Analysis, Dynamical Systems, Fluid Mechanics, Mathematical Physics but above all Nonlinear Control Theory.
In this talk, we discuss the design, analysis, and application of learning models in large-population games. In population games, given a set of strategies, each agent in a population selects a strategy to engage in repeated strategic interactions with others. Rather than computing and adopting the best strategy selection based on a known cost function, the agents need to learn such strategy selection from instantaneous payoffs they receive at each stage of the repeated interactions.
