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 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.
Learning-enabled autonomous systems promise to enable many future technologies such as autonomous driving, intelligent transportation, and robotics. Accelerated by advances in machine learning and AI, there has been tremendous success in the design of learning-enabled autonomous systems. However, these exciting developments are accompanied by new fundamental challenges that arise regarding the safety and reliability of these increasingly complex control systems in which sophisticated algorithms interact with unknown dynamic environments.
Dynamic programming (DP) plays a major role in various fields including reinforcement learning, operations research, computer sciences and of course control engineering. DP allows to solve general optimal control problems in terms of dynamical systems and cost functions. When exploiting DP in a control engineering context, it is often essential to endow the closed-loop system with stability guarantees.
Beyond robustness of asymptotic stability, safety is one of the most important properties to guarantee for a dynamical system. A dynamical system is considered to be safe when trajectories starting from a given set of initial conditions avoid a set of points deemed unsafe.
