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Entropy
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Information Theory in Control Systems, 2nd Edition
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Information Theory in Control Systems, 2nd Edition

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

Deadline for manuscript submissions: 25 December 2024 | Viewed by 3014

Special Issue Editor

Prof. Dr. Adrian-Mihail Stoica

E-Mail Website
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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Related Special Issue

Published Papers (4 papers)

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Research

13 pages, 284 KiB  
Article
Quantum Control Design by Lyapunov Trajectory Tracking and Optimal Control
by Hongli Yang, Guohui Yu and Ivan Ganchev Ivanov
Entropy 2024, 26(11), 978; https://doi.org/10.3390/e26110978 - 15 Nov 2024
Viewed by 386
Abstract
In this paper, we investigate a Lyapunov trajectory tracking design method that incorporates a Schrödinger equation with a dipole subterm and polarizability. Our findings suggest that the proposed control law can overcome the limitations of certain existing control laws that do not converge. [...] Read more.
In this paper, we investigate a Lyapunov trajectory tracking design method that incorporates a Schrödinger equation with a dipole subterm and polarizability. Our findings suggest that the proposed control law can overcome the limitations of certain existing control laws that do not converge. By integrating a quadratic performance index, we introduce an optimal control law, which we subsequently analyze for stability and optimality. We also simulate the spin-1/2 particle system to illustrate our results. These findings are further validated through numerical illustrations involving a 3D, 5D system, and a spin-1/2 particle system. Full article
(This article belongs to the Special Issue Information Theory in Control Systems, 2nd Edition)
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23 pages, 670 KiB  
Article
Distributed Adaptive Optimization Algorithm for High-Order Nonlinear Multi-Agent Stochastic Systems with Lévy Noise
by Hui Yang, Qing Sun and Jiaxin Yuan
Entropy 2024, 26(10), 834; https://doi.org/10.3390/e26100834 - 30 Sep 2024
Viewed by 479
Abstract
An adaptive neural network output-feedback control strategy is proposed in this paper for the distributed optimization problem (DOP) of high-order nonlinear stochastic multi-agent systems (MASs) driven by Lévy noise. On the basis of the penalty-function method, the consensus constraint is removed and the [...] Read more.
An adaptive neural network output-feedback control strategy is proposed in this paper for the distributed optimization problem (DOP) of high-order nonlinear stochastic multi-agent systems (MASs) driven by Lévy noise. On the basis of the penalty-function method, the consensus constraint is removed and the global objective function (GOF) is reconstructed. The stability of the system is analyzed by combining the generalized Itô’s formula with the Lyapunov function method. Moreover, the command filtering mechanism is introduced to solve the “complexity explosion” problem in the process of designing virtual controller, and the filter errors are compensated by introducing compensating signals. The proposed algorithm has been proved that the outputs of all agents converge to the optimal solution of the DOP with bounded errors. The simulation results demonstrate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Information Theory in Control Systems, 2nd Edition)
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19 pages, 602 KiB  
Article
Optimal Estimation of a Signal Generated Using a Dynamical System Modeled with McKean–Vlasov Stochastic Differential Equations
by Vasile Dragan and Samir Aberkane
Entropy 2024, 26(6), 483; https://doi.org/10.3390/e26060483 - 31 May 2024
Viewed by 471
Abstract
We consider, in this paper, the problem of state estimation for a class of dynamical systems governed via continuous-time McKean–Vlasov stochastic differential equations. The estimation problem is stated and solved under an H2 norm setting. We adopt a Riccati-based approach in order [...] Read more.
We consider, in this paper, the problem of state estimation for a class of dynamical systems governed via continuous-time McKean–Vlasov stochastic differential equations. The estimation problem is stated and solved under an H2 norm setting. We adopt a Riccati-based approach in order to solve the optimal estimation problem. Full article
(This article belongs to the Special Issue Information Theory in Control Systems, 2nd Edition)
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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 1027
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, 2nd Edition)
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