Behavioral systems theory in data-driven analysis, signal processing, and control
Ivan Markovsky,Florian Dörfler +1 more
TLDR
Data-driven analysis, signal processing, and control methods as mentioned in this paper can be broadly classified as implicit and explicit approaches, with the implicit approach being more robust to uncertainty and robustness to noise.About:
This article is published in Annual Reviews in Control.The article was published on 2021-11-10 and is currently open access. It has received 38 citations till now. The article focuses on the topics: Robust control & Model predictive control.read more
Citations
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Bridging direct & indirect data-driven control formulations via regularizations and relaxations
TL;DR: In this paper, the authors discuss connections between sequential system identification and control for linear time-invariant systems, often termed indirect data-driven control, as well as a contemporary direct data driven control approach seeking an optimal decision compatible with recorded data assembled in a Hankel matrix and robustified through suitable regularizations.
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Bridging Direct and Indirect Data-Driven Control Formulations via Regularizations and Relaxations
TL;DR: In this article , the authors discuss connections between sequential system identification and control for linear time-invariant systems, often termed indirect data-driven control, as well as a contemporary direct data driven control approach seeking an optimal decision compatible with recorded data assembled in a Hankel matrix and robustified through suitable regularizations.
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Willems’ Fundamental Lemma for Linear Descriptor Systems and Its Use for Data-Driven Output-Feedback MPC
TL;DR: A tailored variant of Willems’ fundamental lemma is given, which shows that for descriptor systems the non-parametric modeling via a Hankel matrix requires less data compared to linear time-invariant systems without algebraic constraints.
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Data-Driven Control of Distributed Event-Triggered Network Systems
TL;DR: In this paper , a distributed event-triggered transmission strategy based on periodic sampling is proposed, under which a model-based stability criterion for the closed-loop network system is derived, by leveraging a discrete-time looped-functional approach.
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Probabilistic design of optimal sequential decision-making algorithms in learning and control
Émiland Garrabé,Giovanni Russo +1 more
TL;DR: A survey of sequential decision-making problems that involve optimizing over probability functions is presented in this article , where the authors discuss the relevance of these problems for learning and control, and present a framework that combines a problem formulation and a set of resolution methods.
References
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Journal ArticleDOI
Data Informativity: A New Perspective on Data-Driven Analysis and Control
TL;DR: This article develops a new framework in order to work with data that are not necessarily persistently exciting, and investigates necessary and sufficient conditions on the informativity of data for several data-driven analysis and control problems.
Proceedings ArticleDOI
Predictive control for agile semi-autonomous ground vehicles using motion primitives
TL;DR: This paper presents a hierarchical control framework for the obstacle avoidance of autonomous and semi-autonomous ground vehicles based on motion primitives created from a four-wheel nonlinear dynamic model.
Journal ArticleDOI
A Flexible Transmission System as a Benchmark for Robust Digital Control
TL;DR: The robustness in stability and in performance of eight solutions to a benchmark problem for digital robust control design which have been presented at the 1995 European Control Conference in Rome are evaluated.
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Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
TL;DR: This tutorial argues that Wasserstein distributionally robust optimization has interesting ramifications for statistical learning and motivates new approaches for fundamental learning tasks such as classification, regression, maximum likelihood estimation or minimum mean square error estimation, among others.
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State Maps for Linear Systems
Paolo Rapisarda,Jan C. Willems +1 more
TL;DR: The paper addresses the problem of computing state variables for systems of linear differential-algebraic equations of various forms and considers the behavioral one.