Calendar

Posted August 18, 2023

Control and Optimization Seminar Questions or comments?

Mario Sznaier, Northeastern University IEEE Fellow, IEEE Control Systems Society Distinguished Member Awardee
Why Do We Need Control in Control Oriented Learning?

Despite recent advances in machine learning (ML), the goal of designing control systems capable of fully exploiting the potential of these methods remains elusive. Modern ML can leverage large amounts of data to learn powerful predictive models, but such models are not designed to operate in a closed-loop environment. Recent results on reinforcement learning offer a tantalizing view of the potential of a rapprochement between control and learning, but so far proofs of performance and safety are mostly restricted to limited cases. Thus, learning elements are often used as black boxes in the loop, with limited interpretability and less than completely understood properties. Further progress hinges on the development of a principled understanding of the limitations of control-oriented machine learning. This talk will present some initial results unveiling the fundamental limitations of some popular learning algorithms and architectures when used to control a dynamical system. For instance, it shows that even though feed forward neural nets are universal approximators, they are unable to stabilize some simple systems. We also show that a recurrent neural net with differentiable activation functions that stabilizes a non-strongly stabilizable system must itself be open loop unstable, and discuss the implications of this for training with noisy, finite data. Finally, we present a simple system where any controller based on unconstrained optimization of the parameters of a given structure fails to render the closed loop system input-to-state stable. The talk finishes by arguing that when the goal is to learn stabilizing controllers, the loss function should reflect closed loop performance, which can be accomplished using gap-metric motivated loss functions, and presenting initial steps towards that goal.

Posted August 18, 2023
Last modified September 11, 2023

Control and Optimization Seminar Questions or comments?

Cristina Pignotti, Università degli Studi dell'Aquila
Consensus Results for Hegselmann-Krause Type Models with Time Delay

We study Hegselmann-Krause (HK) opinion formation models in the presence of time delay effects. The influence coefficients among the agents are nonnegative, as usual, but they can also degenerate. This includes, e.g., the case of on-off influence, namely the agents do not communicate over some time intervals. We give sufficient conditions ensuring that consensus is achieved for all initial configurations. Moreover, we analyze the continuity type equation obtained as the mean-field limit of the particle model when the number of agents goes to infinity. Finally, we analyze a control problem for a delayed HK model with leadership and design a simple control strategy steering all agents to any fixed target opinion.

Posted September 12, 2023

Control and Optimization Seminar Questions or comments?

Melvin Leok, University of California, San Diego
TBA

Posted August 22, 2023

Control and Optimization Seminar Questions or comments?

Eduardo Cerpa, Pontificia Universidad Católica de Chile SIAM Activity Group on Control and Systems Theory Prize Recipient
Control and System Theory Methods in Neurostimulation

Electrical stimulation therapies are used to treat the symptoms of a variety of nervous system disorders. Recently, the use of high frequency signals has received increased attention due to its varied effects on tissues and cells. In this talk, we will see how some methods from Control and System Theory can be useful to address relevant questions in this framework when the FitzHugh-Nagumo model of a neuron is considered. Here, the stimulation is through the source term of an ODE and the level of neuron activation is associated with the existence of action potentials which are solutions with a particular profile. A first question concerns the effectiveness of a recent technique called interferential currents, which combines two signals of similar kilohertz frequencies intended to activate deeply positioned cells. The second question is about how to avoid the onset of undesirable action potentials originated when signals that produce conduction block are turned on. We will show theoretical and computational results based on methods such as averaging, Lyapunov analysis, quasi-static steering, and others.

Posted August 22, 2023

Control and Optimization Seminar Questions or comments?

Philip E. Paré, Purdue University
Modeling, Estimation, and Analysis of Epidemics over Networks

We present and analyze mathematical models for network-dependent spread. We use the analysis to validate a SIS (susceptible-infected-susceptible) model employing John Snow’s classical work on cholera epidemics in London in the 1850’s. Given the demonstrated validity of the model, we discuss control strategies for mitigating spread, and formulate a tractable antidote administration problem that significantly reduces spread. Then we formulate a parameter estimation problem for an SIR (susceptible-infected-recovered) networked model, where costs are incurred by measuring different nodes' states and the goal is to minimize the total cost spent on collecting measurements or to optimize the parameter estimates while remaining within a measurement budget. We show that these problems are NP-hard to solve in general and propose approximation algorithms with performance guarantees. We conclude by discussing an ongoing project where we are developing online parameter estimation techniques for noisy data and time-varying epidemics.

Posted January 18, 2023
Last modified August 22, 2023

Control and Optimization Seminar Questions or comments?

Maruthi Akella, University of Texas Fellow of AIAA, IEEE, and AAS
Sub-Modularity Measures for Learning and Robust Perception in Aerospace Autonomy

Onboard learning and robust perception can be generally viewed to characterize autonomy as overarching system-level properties. The complex interplay between autonomy and onboard decision support systems introduces new vulnerabilities that are extremely hard to predict with most existing guidance and control tools. In this seminar, we review some recent advances in learning-oriented and information-aware path- planning, and sub-modularity metrics for non-myopic sensor scheduling for “plug-and- play” systems. The concept of “learning-oriented” path-planning is realized through certain new classes of exploration inducing distance metrics. These technical foundations will be highlighted through aerospace applications with active learning inside dynamic and uncertain environments.

Posted September 2, 2023

Control and Optimization Seminar Questions or comments?

Sean Meyn, University of Florida Robert C. Pittman Eminent Scholar Chair, IEEE Fellow, IEEE CSS Distinguished Lecturer
TBA

Posted September 8, 2023

Control and Optimization Seminar Questions or comments?

Meeko Oishi, University of New Mexico NSF BRITE Fellow
TBA