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Information Theory in Control Systems
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Information Theory in Control Systems

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 (30 June 2023) | Viewed by 10661

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 between agents is exchanged 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.

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

Related Special Issue

Published Papers (4 papers)

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Research

34 pages, 9432 KiB  
Article
Forward-Backward Sweep Method for the System of HJB-FP Equations in Memory-Limited Partially Observable Stochastic Control
by Takehiro Tottori and Tetsuya J. Kobayashi
Entropy 2023, 25(2), 208; https://doi.org/10.3390/e25020208 - 21 Jan 2023
Cited by 4 | Viewed by 4793
Abstract
Memory-limited partially observable stochastic control (ML-POSC) is the stochastic optimal control problem under incomplete information and memory limitation. To obtain the optimal control function of ML-POSC, a system of the forward Fokker–Planck (FP) equation and the backward Hamilton–Jacobi–Bellman (HJB) equation needs to be [...] Read more.
Memory-limited partially observable stochastic control (ML-POSC) is the stochastic optimal control problem under incomplete information and memory limitation. To obtain the optimal control function of ML-POSC, a system of the forward Fokker–Planck (FP) equation and the backward Hamilton–Jacobi–Bellman (HJB) equation needs to be solved. In this work, we first show that the system of HJB-FP equations can be interpreted via Pontryagin’s minimum principle on the probability density function space. Based on this interpretation, we then propose the forward-backward sweep method (FBSM) for ML-POSC. FBSM is one of the most basic algorithms for Pontryagin’s minimum principle, which alternately computes the forward FP equation and the backward HJB equation in ML-POSC. Although the convergence of FBSM is generally not guaranteed in deterministic control and mean-field stochastic control, it is guaranteed in ML-POSC because the coupling of the HJB-FP equations is limited to the optimal control function in ML-POSC. Full article
(This article belongs to the Special Issue Information Theory in Control Systems)
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15 pages, 465 KiB  
Article
H∞ State-Feedback Control of Multi-Agent Systems with Data Packet Dropout in the Communication Channels: A Markovian Approach
by Adrian-Mihail Stoica and Serena Cristiana Stoicu
Entropy 2022, 24(12), 1734; https://doi.org/10.3390/e24121734 - 28 Nov 2022
Cited by 2 | Viewed by 963
Abstract
The paper presents an H∞ type control procedure for multi-agent systems taking into account possible data dropout in the communication network. The data dropout is modelled using a standard homogeneous Markov chain leading to an H∞ type control problem for stochastic [...] Read more.
The paper presents an H∞ type control procedure for multi-agent systems taking into account possible data dropout in the communication network. The data dropout is modelled using a standard homogeneous Markov chain leading to an H∞ type control problem for stochastic multi-agent systems with Markovian jumps. The considered H∞ type criterion includes, besides the components corresponding to the attenuation condition of exogenous disturbance inputs, quadratic terms aiming to acquire the consensus between the agents. It is shown that in the case of identical agents, a state-feedback controller with Markov parameters may be determined solving two specific systems of Riccati equations whose dimension does not depend on the number of agents. Iterative procedures to solve such systems are also presented together with an illustrative numerical example. Full article
(This article belongs to the Special Issue Information Theory in Control Systems)
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27 pages, 1987 KiB  
Article
Memory-Limited Partially Observable Stochastic Control and Its Mean-Field Control Approach
by Takehiro Tottori and Tetsuya J. Kobayashi
Entropy 2022, 24(11), 1599; https://doi.org/10.3390/e24111599 - 03 Nov 2022
Cited by 6 | Viewed by 2610
Abstract
Control problems with incomplete information and memory limitation appear in many practical situations. Although partially observable stochastic control (POSC) is a conventional theoretical framework that considers the optimal control problem with incomplete information, it cannot consider memory limitation. Furthermore, POSC cannot be solved [...] Read more.
Control problems with incomplete information and memory limitation appear in many practical situations. Although partially observable stochastic control (POSC) is a conventional theoretical framework that considers the optimal control problem with incomplete information, it cannot consider memory limitation. Furthermore, POSC cannot be solved in practice except in special cases. In order to address these issues, we propose an alternative theoretical framework, memory-limited POSC (ML-POSC). ML-POSC directly considers memory limitation as well as incomplete information, and it can be solved in practice by employing the technique of mean-field control theory. ML-POSC can generalize the linear-quadratic-Gaussian (LQG) problem to include memory limitation. Because estimation and control are not clearly separated in the LQG problem with memory limitation, the Riccati equation is modified to the partially observable Riccati equation, which improves estimation as well as control. Furthermore, we demonstrate the effectiveness of ML-POSC for a non-LQG problem by comparing it with the local LQG approximation. Full article
(This article belongs to the Special Issue Information Theory in Control Systems)
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23 pages, 4778 KiB  
Article
Trajectory Tracking within a Hierarchical Primitive-Based Learning Approach
by Mircea-Bogdan Radac
Entropy 2022, 24(7), 889; https://doi.org/10.3390/e24070889 - 28 Jun 2022
Cited by 3 | Viewed by 1297
Abstract
A hierarchical learning control framework (HLF) has been validated on two affordable control laboratories: an active temperature control system (ATCS) and an electrical rheostatic braking system (EBS). The proposed HLF is data-driven and model-free, while being applicable on general control tracking tasks which [...] Read more.
A hierarchical learning control framework (HLF) has been validated on two affordable control laboratories: an active temperature control system (ATCS) and an electrical rheostatic braking system (EBS). The proposed HLF is data-driven and model-free, while being applicable on general control tracking tasks which are omnipresent. At the lowermost level, L1, virtual state-feedback control is learned from input–output data, using a recently proposed virtual state-feedback reference tuning (VSFRT) principle. L1 ensures a linear reference model tracking (or matching) and thus, indirect closed-loop control system (CLCS) linearization. On top of L1, an experiment-driven model-free iterative learning control (EDMFILC) is then applied for learning reference input–controlled outputs pairs, coined as primitives. The primitives’ signals at the L2 level encode the CLCS dynamics, which are not explicitly used in the learning phase. Data reusability is applied to derive monotonic and safely guaranteed learning convergence. The learning primitives in the L2 level are finally used in the uppermost and final L3 level, where a decomposition/recomposition operation enables prediction of the optimal reference input assuring optimal tracking of a previously unseen trajectory, without relearning by repetitions, as it was in level L2. Hence, the HLF enables control systems to generalize their tracking behavior to new scenarios by extrapolating their current knowledge base. The proposed HLF framework endows the CLCSs with learning, memorization and generalization features which are specific to intelligent organisms. This may be considered as an advancement towards intelligent, generalizable and adaptive control systems. Full article
(This article belongs to the Special Issue Information Theory in Control Systems)
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