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.
As the field of Soft Robotics matures, the complexity of the tackled problems will inevitably increase, and computational tools for simulation and optimization will become key aspects of soft robot design and control. In this talk, I will highlight how computation paves the way toward industrial-grade robots that are lightweight and inexpensive, yet functional.
The mission of an unmanned air vehicle (UAV) tethered to a small unmanned surface vehicle (USV) is considered. The tether doubles as a power umbilical and communications link, providing unlimited flight duration and secure communications while limiting mobility. Contrary to the majority of existing tethered UAV work which assumes a taut tether for dynamic stability, this dissertation addresses the challenge of tether management for a slack, hanging tether in a dynamic ocean environment up to sea state 4 on the Douglas scale.
Continuum mechanics provide accurate mechanical models for deformable solids. Numerical tools, like the Finite Element Methods (FEM), solve the partial differential equations with the major drawback of being time consuming. This presentation will show that there are some solutions to make FEM models fast enough to be compatible with real-time simulation and control methods, that can be also mixed with learning approach. For soft robotics, this provides a very powerful tool to help the design and the control, in particular for complex interaction with the environment.
Programmatic advertising is at the heart of the business for companies such as Google, Facebook, and Yahoo. A Demand Side Platform is a particular business model for programmatic advertising, and its goal is to optimally spend an advertising budget. The optimization is challenging due to an underlying high-dimensional, nonlinear, time-varying, dynamic, and stochastic plant. In this talk we introduce the optimization problem and demonstrate how techniques from control engineering can be used to analyze and solve the problem.
Standard stochastic control methods assume that the probability distribution of uncertain variables is available. Unfortunately, in practice, obtaining accurate distribution information is a challenging task.
Reinforcement learning (RL) has been widely used to solve sequential decision making problems in unknown stochastic environments. In this talk we first present a new zeroth-order policy optimization method for Multi-Agent Reinforcement Learning (MARL) with partial state and action observations and for online learning in non-stationary environments. Zeroth-order optimization methods enable the optimization of black-box models that are available only in the form of input-output data and are common in training of Deep Neural Networks and RL.
System identification has a long history with several well-established methods, in particular for learning linear dynamical systems from input/output data. While the asymptotic properties of these methods are well understood as the number of data points goes to infinity or the noise level tends to zero, how well their estimates in finite data regime evolve is relatively less studied. This talk will mainly focus on our analysis of the robustness of the classical Ho-Kalman algorithm and how it translates to non-asymptotic estimation error bounds as a function of the number of data samples.
The aspiration of modern robotics is to achieve a level of adaptability, robustness and safety that will allow the wide deployment of robots in unstructured domains and everyday human spaces. This requires progress at multiple components of robotics, from mechanisms, to sensing as well as decision-making and reasoning. This talk starts from robot planning algorithms, which achieve asymptotic optimality for systems with significant dynamics.
Modern nonlinear control theory seeks to endow systems with properties such as stability and safety. Despite its successful deployment in various domains, uncertainty remains a significant challenge, while data offers a potential solution. In this talk, I will discuss two settings of data-driven nonlinear control, where uncertainty arises from 1) the dynamics model and 2) the sensing model. I will introduce robust control synthesis procedures based on Control Lyapunov and Control Barrier Functions. Using this framework, we will show data-dependent guarantees.
