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.
Direct policy search has achieved great empirical success in reinforcement learning. Many recent studies have revisited its theoretical foundation for continuous control, which reveals elegant nonconvex geometry in various benchmark problems.
Our work is broadly motivated by the emergence of learning-based methods in control theory and robotics, with a specific focus on scenarios that have humans in-the-loop with control systems.
Machine learning is creating new paradigms and opportunities in the design of advanced process control systems for chemical processes.
This talk presents recent results on nonlinear observers (estimation algorithms) and their applications in motion estimation problems ranging from wearable sensors to autonomous vehicles. First, a new observer design technique that integrates the classical high-gain observer with a novel LPV/LMI observer to provide significant advantages compared to both methods is presented.
Data-driven modeling typically involves simplifications of systems through dimensionality reduction (less variables) or through dimensionality enlargement (more variables, but simpler, perhaps linear, dynamics). Autoencoders with narrow bottleneck layers are a typical approach to the former (allowing the discovery of dynamics taking place in a lower-dimensional manifold), while autoencoders with wide layers provi
Safety is a critical requirement for real-world systems, including autonomous vehicles, robots, power grids and more. Over the past decades, many methods have been developed for safety verification and safe control design in deterministic systems.
The development of extremum seeking (ES) has progressed, over the past hundred years, from static maps, to finite-dimensional dynamic systems, to networks of static and dynamic agents. Extensions from ODE dynamics to maps and agents that incorporate delays or even partial differential equations (PDEs) is the next natural step in that progression through ascending research challenges.
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?
