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
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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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System Identification: A Machine Learning Perspective
TL;DR: This research presents a novel approach to estimating functions from sparse and noisy data that exploits Tikhonov regularization theory and its applications in reinforcement learning.
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From noisy data to feedback controllers: non-conservative design via a matrix S-lemma
TL;DR: A new method to obtain feedback controllers of an unknown dynamical system directly from noisy input/state data and derive nonconservative design methods for quadratic stabilization, and data-based linear matrix inequalities, enables control design from large datasets.
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Data-driven control of complex networks
TL;DR: This paper develops a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics, and proves its controls are provably correct for networks with linear dynamics.
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Structured Low-Rank Approximation with Missing Data
TL;DR: This paper considers low-rank approximation of affinely structured matrices with missing elements, a singular linear least-norm problem, based on reformulation of the problem as inner and outer optimization.
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Global total least squares modeling of multivariable time series
Berend Roorda,Christiaan Heij +1 more
TL;DR: Attractive aspects of the proposed method are that the model error is measured globally, it can be applied for multi-input, multi-output systems, and no prior distinction between inputs and outputs is required.