Dynamic Systems & Controls

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.

A romp through the philosophy of modeling for feedback control design
awaits the audience. The target material will focus on the stringent requirements of
experiment design and data content management to achieve models just barely
adequate for high-performance control design. The crux of the matter is that, for this
case, the data need to be informative for a new purpose and its associated novel
closed-loop operating regime. This necessitates bringing in information about the

The recent and rapid emergence of disruptive technologies is dramatically changing how traffic is monitored and managed in our cities. They will contribute to generate new knowledge and capabilities to design and implement innovative transport policies. In this talk, we will show how we can exploit new technologies to improve traffic management. We will focus on control strategies for traffic systems with the aid of small fleets of connected and automated vehicles immersed in human driven traffic flow.

This talk presents results on stochastic approximations of hybrid dynamical systems. It starts with an overview of hybrid systems. These systems involve state variables that sometimes change continuously and sometimes jump. The solutions to a hybrid system are subject to the interplay of the system’s flow set, flow map, jump set, and jump map. In the context of this talk, a stochastic approximation of a hybrid system is one where an iterative algorithm replicates, approximately and in average or expected value, the effect of the continuous-time flow map on the flow set.

            In this talk we will discuss estimation entropy for continuous-time nonlinear systems, which is a variant of topological entropy formulated in terms of the number of functions that approximate all system trajectories up to an exponentially decaying error.

Boundary Port Hamiltonian Systems have been introduced in [9] as an extension of Hamiltonian systems defined with respect to Hamiltonian differential operators, to open systems which may interact with their environment through the boundary of their spatial domain and have led to numerous applications for thei

Battery degradation is at the crux of the total cost of ownership and the lifetime GHG abatement of Electric Vehicles (EVs) and Battery Energy System Storage (BESS).  Currently, the battery State of Health (SOH) is quantified by capacity (cyclable energy) and cell resistance (power capability).