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Information Theory in Control Systems II

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 1169

Special Issue Editor


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Guest Editor
Faculty of Aerospace Engineering, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: control systems; optimal control; estimation and filtering; robust control; stochastic systems; fault detection and isolation; automatic flight control systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

An important area of investigation in control science is that of networked multiagent systems. Information is exchanged between agents through communication channels subject to various imperfections, including delay transmissions, lost packets, data quantization, and decentralized architectures. Interaction between control systems and information theory has thus become a challenging task which has received much attention over the last few years.

The aim of this Special Issue on “Information Theory in Control Systems” is to present new theoretical developments and potential applications bridging the areas of control, communications, and information theory.

Topics of the issue include, without being restricted to, the following:

  • Networked control systems under communication constraints;
  • Estimation and filtering theory for multisensor systems;
  • Sampled-data control for networked control systems;
  • Stochastic optimal control with randomized control strategies;
  • Entropy-based approaches in optimal control;
  • Feedback control, state-estimation, and consensus problems for multiagent systems;
  • Entropy methods in estimation problems;
  • Fault-tolerant control design for networked control systems with communication constraints;
  • Feedback control under fading communication channels;
  • Event-triggered control and filtering for multiagent systems;
  • Security control of networked systems under data availability and integrity attacks.

Prof. Dr. Adrian-Mihail Stoica
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multiagent systems
  • optimal control
  • optimal estimation and filtering
  • communication channel constraints
  • decentralized control

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Published Papers (1 paper)

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12 pages, 829 KiB  
Article
Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities
by Adrian-Mihail Stoica and Isaac Yaesh
Entropy 2024, 26(2), 115; https://doi.org/10.3390/e26020115 - 26 Jan 2024
Viewed by 777
Abstract
The paradigm of stochastic antiresonance is considered for a class of nonlinear systems with sector bounded nonlinearities. Such systems arise in a variety of situations such as in engineering applications, in physics, in biology, and in systems with more general nonlinearities, approximated by [...] Read more.
The paradigm of stochastic antiresonance is considered for a class of nonlinear systems with sector bounded nonlinearities. Such systems arise in a variety of situations such as in engineering applications, in physics, in biology, and in systems with more general nonlinearities, approximated by a wide neural network of a single hidden layer, such as the error equation of Hopfield networks with respect to equilibria or visuo-motor tasks. It is shown that driving such systems with a certain amount of state-multiplicative noise, one can stabilize noise-free unstable systems. Linear-Matrix-Inequality-based stabilization conditions are derived, utilizing a novel non-quadratic Lyapunov functional and a numerical example where state-multiplicative noise stabilizes a nonlinear system exhibiting chaotic behavior is demonstrated. Full article
(This article belongs to the Special Issue Information Theory in Control Systems II)
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