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 control engineering practice, the users of system identification may have prior knowledge of control plants to be identified. In order to get better model estimates and further improve the modeling efficiency, the users have thus been suggested to make an intelligent choice of experiment design, model set, and identification criterion guided by prior knowledge as well as by observed data.
Recent research in safety control has leveraged the availability of accurate models to detect impending safety violations and to intervene accordingly. However, there is often a mismatch between the models that are used for algorithm design and the real systems. Moreover, control designs typically assume the availability of full state information that is error-free and trustworthy.
In this talk, we will present some of our recent results and ongoing work on safety-critical control synthesis under state and time (spatiotemporal) constraints and input constraints, with some applications in multi-robot systems. The proposed framework aims to eventually develop and integrate estimation, learning, and control methods towards provably-correct and computationally-efficient mission synthesis for multi-agent systems in the presence of spatiotemporal constraints and uncertainty.
The ability of machine learning techniques to process rich sensory inputs such as vision makes them highly appealing for use in robotic systems (e.g., micro aerial vehicles and robotic manipulators). However, the increasing adoption of learning-based components in the robotics perception and control pipeline poses an important challenge: how can we guarantee the safety and performance of such systems?
We consider the Bayesian inverse problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken after the initial time. In practical applications, the large dimension of the dynamical system state poses a computational obstacle to computing the exact posterior distribution.
The introduction of machine learning (ML) and artificial intelligence (AI) creates unprecedented opportunities for achieving full autonomy. However, learning-based methods in building autonomous systems can be extremely brittle in practice and are not designed to be verifiable. In this talk, I will present several of our recent efforts that combine ML with formal methods and control theory to enable the design of provably dependable and safe autonomous systems.
In this talk, I will present our recent results on compositional verification and synthesis of interconnected control systems against high-level temporal logic requirements. I propose a divide and conquer strategy to scale our proposed techniques by leveraging the natural structure present in the system to break the verification and synthesis problem into semi-independent ones. I will leverage small-gain type reasoning and notions of barrier certificates as two key tools to tackle the verification and synthesis complexity.
Our understanding of multi-robot coordination and control has experienced great advances to the point where deploying multi-robot systems in the near future seems to be a
Increased autonomy can have many advantages, including increased safety and reliability, improved reaction time and performance, reduced personnel burden with associated cost savings, and the ability to continue operations in communications-degraded or denied environments. Artificial Intelligence for Small Unit Maneuver (AISUM) envisions a way for future expeditionary tactical maneuver elements to team with intelligent adaptive systems.
In this talk we discuss probabilistic formulations of system identification, with particular focus on handling sparse, noisy, and indirect data. We introduce the problem from a Bayesian perspective and discuss how it provides a principled mechanism for fusing information and data. We can extract estimators of the system from the posterior distribution, and compare them to commonly used least squares-based optimization objectives in the literature ranging from Hankel Matrix/ Markov parameter based methods to sparse identification of nonlinear dynamcs (SINDy) to dynamic mode decomposition.
