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.
Journal ArticleDOI
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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On Direct vs Indirect Data-Driven Predictive Control
TL;DR: In this article, the authors compare the direct and indirect approaches to data-driven predictive control of stochastic linear time-invariant systems and reveal the existence of two distinct regimes of performance as the size of the dataset of input-output behaviors is increased.
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Data-Driven Synthesis of Optimization-Based Controllers for Regulation of Unknown Linear Systems
TL;DR: In this article, the steady-state transfer function of a linear system can be computed from data samples without any knowledge or estimation of the system model, and a data-driven representation is used to design a controller, inspired by a gradient-descent optimization method, that regulates the system to the solution of a convex optimization problem.
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Robust Data-Enabled Predictive Control: Tractable Formulations and Performance Guarantees.
TL;DR: In this article, robust data-enabled predictive control (DeePC) is proposed to obtain model-free optimal control for LTI systems based on noisy input/output data, where robust DeePC solves a min-max optimization problem to compute the optimal control sequence that is resilient to all possible realizations of the uncertainties in the input and output data within a prescribed uncertainty set.
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Data-driven model predictive control: closed-loop guarantees and experimental results
TL;DR: In this article, the authors provide a comprehensive review and practical implementation of a recently developed model predictive control (MPC) framework for controlling unknown systems using only measured data and no explicit model knowledge, relying on an implicit system parametrization from behavioral systems theory based on one measured input-output trajectory.
Proceedings ArticleDOI
The behavioural approach to distributed systems
H.K. Pillai,Jan C. Willems +1 more
TL;DR: In this article, a behavioural approach to N-D systems is introduced, which can then be made use of in their companion paper (see ibid.) which extends the theory of storage functions and dissipation to N -D systems.