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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An application of system identification in metrology
TL;DR: The approach is to model the measurement process as a step response of a dynamical system, where the input step level is the quantity of interest, and proposes an algorithm that does real-time processing of the sensor's measurements.
Proceedings ArticleDOI
Data Driven Control: An Offset Free Approach
TL;DR: It is shown that offset free control (zero mean tracking error) is achieved under the assumption that the underlying dynamics are linear and the closed loop trajectories of the database are in turn offset free.
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System Representation and Optimal Control in Input-Output Data Space
TL;DR: In this paper, a linear time-invariant plant in the input-output data space is considered, where the plant dynamics is represented as a set of basis vectors whose elements are input-input data of the plant.
Journal ArticleDOI
Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping
TL;DR: In this paper, a data-enabled predictive control (DeePC) algorithm is employed in voltage source converter (VSC) based high-voltage DC (HVDC) stations to perform safe and optimal wide-area control for power system oscillation damping.
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Robust stability analysis of a simple data-driven model predictive control approach
TL;DR: In this paper, the authors provide a theoretical analysis of closed-loop properties of a simple data-driven model predictive control (MPC) scheme, which relies on an implicit description of linear time-invariant systems based on behavioral systems theory.