Dear all, As previously announced at the information coffee, Olle's dissertation on October 11 will be preceded by a seminar series in the afternoon of October 10. See below.

Welcome! /Anders

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October 10 at 13:15-14:00 in the Seminar Room M:3170-73 in the M-building, LTH.

Jack Umenberger, Oxford University

Title: No such thing as a model-free lunch? Model-free search and reliable decision making.

Abstract: Inspired by breakthrough results in the processing of complex data, machine learning is being increasingly applied to problems in decision-making and control. However, to be suitable for deployment in applications, we require learning-based algorithms that come with guarantees of reliability, robustness, and safety.

This talk will focus on the reliability of model-free policy search, addressing the question: when do such methods find optimal solutions, and when do they get trapped in poor local minima? Existing work has considered static policies; however, for dynamic policies that remember past observations - necessary for optimal decision making in many applications - these questions have hitherto remained unanswered. Focusing on the classic control-theoretic problem of output estimation, I will present the first model-free policy search algorithm for dynamic policies guaranteed to converge to the optimal solution.

Along the way, I’ll also describe my path toward working on this problem, highlighting some of my contributions to model-based approaches for safe and reliable control, including data-driven robust control, system identification, and trajectory optimization. I will offer my perspective on the strengths and weaknesses of model-free and model-based methods, as well as the ways in which they complement each other.

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October 10 at 14:05-14:50

Håkan Hjalmarsson, KTH

Title: Data-driven control: A statistical decision theory perspective

There are many different methods for deriving a controller from a given data.set but little guidance is provided in the literature in terms of which methods are preferrable from a performance point of view. In this talk we focus on the information aspects of the problem. We use existing results from decision theory to establish necessary and sufficient conditions for a data-driven controller that guarantees that there is no other data-driven controller that uniformly (regardless of the underlying system) can outperform it in terms of average control cost. We discuss issues like direct vs indirect data-driven control from this perspective.

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October 10 at 15:30-16:15

Valentina Breschi, TU Eindhoven

Title: Philosophies, promises and challenges of direct data-driven control

Abstract: Direct data-driven control (DDC) has become a central focus in control research, advocating the direct use of data to achieve desired control objectives and, eventually, satisfy design constraints. By exploring a selected set of DDC techniques, this talk provides a (personal) take on the philosophy behind data-driven control, highlighting some of the promises that DDC approaches have already fulfilled and spotlighting some of the challenges yet to be faced.

Bio: Valentina Breschi received her B.Sc. in Electronic and Telecommunication Engineering and her M.Sc. in Electrical and Automation Engineering from the University of Florence (Italy) in 2011 and 2014, respectively. She received her Ph.D. in Control Systems from IMT School for Advanced Studies Lucca (Italy) in 2018. From January to July 2017, she was a visiting scholar at the Department of Aerospace Engineering, University of Michigan (United States). From 2018 to 2020, she was a post-doctoral researcher at Politecnico di Milano (Italy), where she then held a fixed-term position as a junior Assistant Professor from 2020 to 2023. In 2023, she joined the Department of Electrical Engineering at Eindhoven University of Technology (The Netherlands) as an Assistant Professor. Her main research interests include data-driven control, system identification, with a focus on piecewise affine and switching systems, collaborative learning, and human-centered policy design, focusing on mobility systems.

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October 10 at 16:20-17:05

Babak Hassibi, Caltech

Title: Distributionally Robust Control

Abstract: Traditional methods in control either assume that the disturbances are random processes with known statistics (stochastic or H_2 control) or that they are adversarial (robust or H_infinity control). Recently, there has been growing interest in a framework, called distributional robustness, that can interpolate between these two extremes. In distributionally robust control one assumes that the disturbances have a distribution that lies in a given uncertainty set and the goal is to design a controller that minimizes the worst case expected control cost over all distributions in this set. A not-very-well-known fact is that, if the uncertainty set is a Kullback-Liebler ball around a nominal Gaussian distribution, then distributionally-optimal controllers are given by central H_infinity controllers. These days Wasserstein-2 balls are mostly considered for distributional robustness because, unlike KL divergence, they do not require the support of all the distributions within the ball to be identical, and because of their connections to optimal transport theory. In this talk we consider the problem of designing distributionally robust optimal (DRO) controllers in the Wasserstien-2 metric. We characterize the optimal control policy and show that, although it lacks a finite-order state-space realization (i.e., it is non-rational), it can be characterized by a finite-dimensional parameter. Leveraging this, we develop an efficient frequency-domain algorithm to compute the optimal control policy and present a convex optimization method to construct a rational state-space controller that best approximates the optimal non-rational controller in the H_infinity norm. This approach leads to efficiently computable control strategies and we demonstrate its advantages over conventional H_2 and H_infinity control through several examples.

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