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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From experiment design to closed-loop control
TL;DR: It is argued that a guiding principle should be to model as well as possible before any model or controller simplifications are made as this ensures the best statistical accuracy.
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A Tour of Reinforcement Learning: The View from Continuous Control
TL;DR: The authors surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications, and reviews the general formulation, terminology, and techniques for reinforcement learning for continuous control.
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On the Sample Complexity of the Linear Quadratic Regulator
TL;DR: This paper proposes a multi-stage procedure that estimates a model from a few experimental trials, estimates the error in that model with respect to the truth, and then designs a controller using both the model and uncertainty estimate, and provides end-to-end bounds on the relative error in control cost.
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Maneuver-based motion planning for nonlinear systems with symmetries
TL;DR: An approach for the efficient solution of motion-planning problems for time-invariant dynamical control systems with symmetries, such as mobile robots and autonomous vehicles, under a variety of differential and algebraic constraints on the state and on the control input.
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Data-Enabled Predictive Control: In the Shallows of the DeePC
TL;DR: In this paper, a data-enabled predictive control (DeePC) algorithm is presented that computes optimal and safe control policies using real-time feedback driving the unknown system along a desired trajectory while satisfying system constraints.