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Journal ArticleDOI

Feedback for physicists: A tutorial essay on control

John Bechhoefer
- 31 Aug 2005 - 
- Vol. 77, Iss: 3, pp 783-836
TLDR
In this paper, a tutorial essay aims to give enough of the formal elements of control theory to satisfy the experimentalist designing or running a typical physics experiment and enough to satisfy a theorist wishing to understand its broader intellectual context.
Abstract
Feedback and control theory are important ideas that should form part of the education of a physicist but rarely do. This tutorial essay aims to give enough of the formal elements of control theory to satisfy the experimentalist designing or running a typical physics experiment and enough to satisfy the theorist wishing to understand its broader intellectual context. The level is generally simple, although more advanced methods are also introduced. Several types of applications are discussed, as the practical uses of feedback extend far beyond the simple regulation problems where it is most often employed. Sketches are then provided of some of the broader implications and applications of control theory, especially in biology, which are topics of active research.

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Citations
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Book ChapterDOI

Implementation and Tuning of an Optical Tweezers Force-Clamp Feedback System.

TL;DR: The basics of a software-implemented proportional-integral-derivative (PID) controller, how to tune it, and how to determine its optimal feedback rate are described.
Journal ArticleDOI

Cavityless self-organization of ultracold atoms due to the feedback-induced phase transition.

TL;DR: This work proposes and theoretically investigates a system possessing a feedback-induced phase transition that contains a Bose-Einstein condensate placed in an optical potential with the depth that is feedback-controlled according to the intensity of the Bragg-reflected probe light.
DissertationDOI

Stochastic thermodynamics of information processing: bipartite systems with feedback, signal inference and information storage

David Hartich
TL;DR: A framework for two continuously coupled systems, which includes information and refines the standard second law of thermodynamics of bipartite systems is developed, and a purely information theoretic quantity, which is called sensory capacity, is introduced.
Journal ArticleDOI

Reduced dynamics for delayed systems with harmonic or stochastic forcing

TL;DR: A non-homogeneous center manifold (CM) reduction scheme is developed, which allows the derivation of a time-dependent order parameter equation in finite dimension that captures the major dynamical features of the delayed system.
Journal ArticleDOI

Information thermodynamics for interacting stochastic systems without bipartite structure

TL;DR: The generalization of information-theoretic measures, such as learning rates and transfer entropy rates, to this situation is non-trivial and also involves introducing several additional rates, and an important relation linking information theory and estimation theory is generalized.
References
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Book

Elements of information theory

TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Book

System Identification: Theory for the User

Lennart Ljung
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI

Statistical mechanics of complex networks

TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI

The Structure and Function of Complex Networks

Mark Newman
- 01 Jan 2003 - 
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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