Integrated Information Systems for Smart Industrial Electronics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 486

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Guest Editor
Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria
Interests: information systems and technologies; business intelligence; big data; intelligent software agents; machine learning; data mining; multi-criteria decision making; fuzzy sets
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Special Issue Information

Dear Colleagues,

Smart industrial electronics are increasingly embedded in connected, data-intensive and adaptive industrial environments. This evolution is driven by the tighter integration of industrial electronics with enterprise and operational information systems—such as IIoT platforms, SCADA, MES, ERP, PLM, edge/cloud infrastructures and industrial data platforms—enabling real-time visibility, automated coordination and more informed control and decision-making.

This Special Issue focuses on architectures, methods and applications that advance the integration of information systems with smart industrial electronics. We aim to highlight robust, interoperable and secure solutions that support industrial monitoring, diagnostics, optimization and operational excellence across manufacturing and industrial services. Contributions that combine technical depth with validated implementations, prototypes, or industrial case studies are especially encouraged.

  • Submissions may include, but are not limited to, the following topics:
  • Integration architectures linking SCADA/MES/ERP/PLM with smart industrial devices and controllers;
  • Industrial IoT platforms, industrial data pipelines and event-driven industrial information systems;
  • Edge–cloud collaboration for low-latency monitoring, analytics and control;
  • Data acquisition, sensor networks, condition monitoring and predictive maintenance systems;
  • AI/ML for fault detection, anomaly detection, quality prediction and process optimization;
  • Interoperability, data models and standards (e.g., OPC UA, industrial middleware, digital thread concepts);
  • Real-time communication, reliability, safety and resilience in integrated industrial environments;
  • Cybersecurity, privacy and trust for connected industrial electronics and information systems;
  • Human–machine interaction (HMI), decision support and visualization for industrial operations;
  • Reference architectures, benchmarking, datasets and comparative evaluations;
  • Industrial use cases and pilot deployments with measurable operational impact.

We welcome original research articles and high-quality review papers. By fostering interdisciplinary exchange, this Special Issue aims to advance integrated information system solutions that enhance the performance, reliability and security of smart industrial electronics in modern industry.

Prof. Dr. Galina Ilieva
Guest Editor

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Keywords

  • integrated information systems
  • smart industrial electronics
  • industry 4.0
  • industrial IoT
  • SCADA
  • MES
  • ERP
  • PLM
  • edge computing
  • cloud computing
  • interoperability
  • OPC UA
  • industrial analytics
  • predictive maintenance
  • anomaly detection
  • cybersecurity
  • decision support

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Published Papers (1 paper)

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Research

11 pages, 315 KB  
Article
Lyapunov Stability Analysis of a Generated UAV Controller
by Christopher Carr, Miguel Martínez-García, Matthew Coombes and Eve Zhang
Electronics 2026, 15(13), 2898; https://doi.org/10.3390/electronics15132898 - 2 Jul 2026
Viewed by 168
Abstract
A Large Language Model-based search for controller synthesis can yield UAV controllers with strong trajectory-tracking performance. However, low tracking error does not necessarily demonstrate closed-loop stability. This study presents a Lyapunov stability assessment of an automatically generated UAV controller produced through a Large [...] Read more.
A Large Language Model-based search for controller synthesis can yield UAV controllers with strong trajectory-tracking performance. However, low tracking error does not necessarily demonstrate closed-loop stability. This study presents a Lyapunov stability assessment of an automatically generated UAV controller produced through a Large Language Model-based search process. The closed-loop system is numerically linearised about the hover equilibrium, yielding a local closed-loop state matrix Ad.Eigenvalue analysis is then used to determine whether Ad is Schur stable, corresponding to all eigenvalues lying inside the unit circle ρ(Ad)<1. A quadratic Lyapunov function is constructed by solving the discrete-time Lyapunov equation AdTPAdP=Q. The positive definiteness of the resulting matrix provides a local Lyapunov certificate for the linearised closed-loop system. To connect this local certificate to dynamic flight behaviour, the Lyapunov function is evaluated along trajectory-tracking logs using the tracking-error state. The mean Lyapunov value, maximum Lyapunov value, discrete Lyapunov difference, and mean squared error are used to compare the generated controller with PID, LQR, and PID + DOB baselines. The results show that the generated controller satisfies local Lyapunov stability conditions near hover. Our findings demonstrate that established Lyapunov tools can be applied post hoc to a search-generated UAV controller, providing evidence of local stability. Full article
(This article belongs to the Special Issue Integrated Information Systems for Smart Industrial Electronics)
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