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29 pages, 3152 KB

Open AccessArticle

Fuzzy Model-Based Output Constraint Satisfaction Mechanism for Controllers of Nonlinear Processes

by Piotr Marusak and Ewa Niewiadomska-Szynkiewicz

Appl. Sci. 2026, 16(2), 928; https://doi.org/10.3390/app16020928 - 16 Jan 2026

Viewed by 225

Abstract

This paper introduces a novel output constraint satisfaction mechanism that can be used to supplement controllers employing a control law. This mechanism is dedicated to control systems of nonlinear processes, with this additional feature. It utilizes an easy-to-obtain fuzzy model composed of step [...] Read more.

This paper introduces a novel output constraint satisfaction mechanism that can be used to supplement controllers employing a control law. This mechanism is dedicated to control systems of nonlinear processes, with this additional feature. It utilizes an easy-to-obtain fuzzy model composed of step responses, which includes values of the operating points at which these step responses were obtained. The mechanism is based on a prediction approach from Model Predictive Control (MPC) algorithms. Despite this, it can be used with relatively simple controllers (e.g., fuzzy analytical MPC, PID, or Internal Model Control ones). The mechanism involves skillfully modifying the control signal generated by the controller. It is designed in such a way that, under favorable circumstances, the output constraints are not violated, but in less favorable circumstances, the constraint violation can be minimized. The performance and advantages of our mechanism are demonstrated in the simulated control system of an example nonlinear control plant. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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40 pages, 6863 KB

Open AccessArticle

Hybrid Usability Evaluation of an Automotive REM Tool: Human and LLM-Based Heuristic Assessment of IBM Doors Next

by Oana Rotaru, Ciprian Orhei and Radu Vasiu

Appl. Sci. 2026, 16(2), 723; https://doi.org/10.3390/app16020723 - 9 Jan 2026

Viewed by 486

Abstract

Requirements Engineering and Management (REM) tools play a significant role in ensuring project compliance and efficiency. Automotive engineering must comply with regulatory standards, requiring detailed documentation, rigorous testing, and solid traceability. Despite their importance, REM tools are underexplored from the usability and user [...] Read more.

Requirements Engineering and Management (REM) tools play a significant role in ensuring project compliance and efficiency. Automotive engineering must comply with regulatory standards, requiring detailed documentation, rigorous testing, and solid traceability. Despite their importance, REM tools are underexplored from the usability and user experience perspective (UX), even though poor usability can hinder development workflows across stakeholder teams. This study presents a case study of heuristic usability evaluation of IBM DOORS Next Generation, conducted with expert evaluators, using Nielsen’s 10 Usability Heuristics as an evaluation framework. The identified issues were analyzed in terms of impacted heuristics and severity ratings. Additionally, we underwent a Large Language Model (LLM)-based heuristic evaluation, using ChatGPT-5, prompted with the same heuristic set and static screenshots. The LLM detected several issues overlapping with human findings (32%), as well as new ones (23%); therefore, 55% of its outputs are considered valid and 45% are unconfirmed. This highlights both the potential and limitations of AI-driven usability assessment. Overall, the findings underscore the usability challenges of REM tools and suggest that LLMs may serve as complementary evaluators, accelerating early-stage heuristic inspections in safety-critical engineering environments. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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25 pages, 5575 KB

Open AccessArticle

Multi-Agent Multimodal Large Language Model Framework for Automated Interpretation of Fuel Efficiency Analytics in Public Transportation

by Zhipeng Ma, Ali Rida Bahja, Andreas Burgdorf, André Pomp, Tobias Meisen, Bo Nørregaard Jørgensen and Zheng Grace Ma

Appl. Sci. 2025, 15(21), 11619; https://doi.org/10.3390/app152111619 - 30 Oct 2025

Viewed by 2039

Abstract

Enhancing fuel efficiency in public transportation requires the integration of complex multimodal data into interpretable, decision-relevant insights. However, traditional analytics and visualization methods often yield fragmented outputs that demand extensive human interpretation, limiting scalability and consistency. This study presents a multi-agent framework that [...] Read more.

Enhancing fuel efficiency in public transportation requires the integration of complex multimodal data into interpretable, decision-relevant insights. However, traditional analytics and visualization methods often yield fragmented outputs that demand extensive human interpretation, limiting scalability and consistency. This study presents a multi-agent framework that leverages multimodal large language models (LLMs) to automate data narration and energy insight generation. The framework coordinates three specialized agents, including a data narration agent, an LLM-as-a-judge agent, and an optional human-in-the-loop evaluator, to iteratively transform analytical artifacts into coherent, stakeholder-oriented reports. The system is validated through a real-world case study on public bus transportation in Northern Jutland, Denmark, where fuel efficiency data from 4006 trips are analyzed using Gaussian Mixture Model clustering. Comparative experiments across five state-of-the-art LLMs and three prompting paradigms identify GPT-4.1 mini with Chain-of-Thought prompting as the optimal configuration, achieving 97.3% narrative accuracy while balancing interpretability and computational cost. The findings demonstrate that multi-agent orchestration significantly enhances factual precision, coherence, and scalability in LLM-based reporting. The proposed framework establishes a replicable and domain-adaptive methodology for AI-driven narrative generation and decision support in energy informatics. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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25 pages, 5773 KB

Open AccessArticle

Mobile Data Visualisation Interface Design for Industrial Automation and Control: A User-Centred Usability Study

by Chih-Feng Cheng, Chiuhsiang Joe Lin and I-Chin Liu

Appl. Sci. 2025, 15(19), 10832; https://doi.org/10.3390/app151910832 - 9 Oct 2025

Cited by 1 | Viewed by 1399

Abstract

With the increasing integration of mobile technologies into manufacturing automation environments, the effective visualisation of data on small-screen devices has emerged as an important consideration. This study investigates the usability and readability of common visualisation types (bar charts, line charts, and tables) on [...] Read more.

With the increasing integration of mobile technologies into manufacturing automation environments, the effective visualisation of data on small-screen devices has emerged as an important consideration. This study investigates the usability and readability of common visualisation types (bar charts, line charts, and tables) on mobile devices, comparing different interface designs and interaction methods. Using a within-subject experimental design with 11 participants, we evaluated two primary approaches for handling large visualisations on mobile screens: segmented (cutting) displays versus continuous (dragging) displays. Results indicate that segmented displays generally improve task completion time and reduce mental workload for bar charts and tables. In contrast, line charts exhibit more complex patterns that depend on the size of the data. These findings provide practical guidelines for designing responsive data visualisations optimised for mobile interfaces. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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14 pages, 7266 KB

Open AccessArticle

Investigation of Control Systems of FOPDT Plants with Dynamics Asymmetry

by Algirdas Baskys

Appl. Sci. 2025, 15(19), 10770; https://doi.org/10.3390/app151910770 - 7 Oct 2025

Viewed by 730

Abstract

This work investigates feedback control systems with first order plus dead time (FOPDT) plants, which are characterized by asymmetric dynamics. The term asymmetric dynamics is understood to mean that the dynamics of the response of the controlled parameter of the plant to the [...] Read more.

This work investigates feedback control systems with first order plus dead time (FOPDT) plants, which are characterized by asymmetric dynamics. The term asymmetric dynamics is understood to mean that the dynamics of the response of the controlled parameter of the plant to the rise and fall of the plant control signal are different. The novelty of the current work is that it analyzes a case where the asymmetry is introduced by both dynamic parameters of the FOPDT plant: by the asymmetry of the time constant and by the asymmetry of the response delay. Another novelty is that in the proposed asymmetrical PI (aPI) controller, the change in the plant control signal time derivative sign is used to determine the moments for the switching of controller parameters. The use of an aPI controller instead of a conventional PI controller allows us to improve the quality of the control of plants with asymmetric dynamics. It is also important that the problem is solved using a PI type controller, which automation engineers are well aware of and know how to tune its parameters to the dynamics of the plant. Therefore, an aPI controller can be attractive in practical applications. All investigations were performed using Matlab/Simulink software (version R2021b). Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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18 pages, 3072 KB

Open AccessArticle

Enhancing Robotics Education Through XR Simulation: Insights from the X-RAPT Training Framework

by David Mulero-Pérez, Beatriz Zambrano-Serrano, Enrique Ruiz Zúñiga, Michael Fernandez-Vega and Jose Garcia-Rodriguez

Appl. Sci. 2025, 15(18), 10020; https://doi.org/10.3390/app151810020 - 13 Sep 2025

Cited by 1 | Viewed by 2040

Abstract

Extended reality (XR) technologies are gaining traction in technical education due to their potential for creating immersive and interactive training environments. This study presents the development and empirical evaluation of X-RAPT, a collaborative VR-based platform designed to train students in industrial robotics programming. [...] Read more.

Extended reality (XR) technologies are gaining traction in technical education due to their potential for creating immersive and interactive training environments. This study presents the development and empirical evaluation of X-RAPT, a collaborative VR-based platform designed to train students in industrial robotics programming. The system enables multi-user interaction, cross-platform compatibility (VR and PC), and real-time data logging through a modular simulation framework. A pilot evaluation was conducted in a vocational training institute with 15 students performing progressively complex tasks in alternating roles using both VR and PC interfaces. Performance metrics were captured automatically from system logs, while post-task questionnaires assessed usability, comfort, and interaction quality. The findings indicate high user engagement and a distinct learning curve, evidenced by progressively shorter task completion times across levels of increasing complexity. Role-based differences were observed, with main users showing greater interaction frequency but both roles contributing meaningfully. Although hardware demands and institutional constraints limited the scale of the pilot, the findings support the platform’s potential for enhancing robotics education. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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Review

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27 pages, 2003 KB

Open AccessReview

The Convergence of Federated Learning, Knowledge Graphs, and Large Language Models for Language Learning: A Scoping Review

by Michael Kenteris and Konstantinos Kotis

Appl. Sci. 2026, 16(5), 2611; https://doi.org/10.3390/app16052611 - 9 Mar 2026

Viewed by 619

Abstract

Large Language Models (LLMs) in Intelligent Computer-Assisted Language Learning enable highly personalized learning, yet raise significant challenges related to pedagogical grounding, data privacy, and instructional validity. Although Knowledge Graphs (KGs) and Federated Learning (FL) can mitigate these issues in isolation, evidence on systematic [...] Read more.

Large Language Models (LLMs) in Intelligent Computer-Assisted Language Learning enable highly personalized learning, yet raise significant challenges related to pedagogical grounding, data privacy, and instructional validity. Although Knowledge Graphs (KGs) and Federated Learning (FL) can mitigate these issues in isolation, evidence on systematic FL–KG–LLM integration for educational language learning remains limited. This scoping review maps the FL–KG–LLM convergence landscape. Following PRISMA-ScR guidelines, we searched six databases and screened 51 papers (2019–2025) using automated extraction. Our findings indicate limited convergence: no papers integrate all three domains, and 58.8% of approaches remain confined to isolated technological silos. Reporting is also uneven across the corpus, with an average “Not Reported” (NR) rate of 84.5%, most notably for privacy mechanisms (92.2%), validation metrics (90.2%), and Common European Framework of Reference for Languages (CEFR) alignment (88.2%). Domain-specific analysis reveals two distinct patterns: inter-domain gaps (disciplinary silos resulting in expected CEFR absence in single-domain papers) and intra-domain gaps (failure to report domain-critical variables, including 100% parameter NR in FL studies, 86.7% validation NR in KG studies, and 100% CEFR NR in convergence papers). Taken together, these gaps suggest that pedagogical grounding is treated as optional rather than structural. We therefore identify two pillars of pedagogical grounding: a Grounding Pillar, which constrains LLM outputs via Knowledge Graph rules, and a Validation Pillar, which concerns how authoritative frameworks (e.g., CEFR) are mapped onto Knowledge Graph schemas and evaluated. The near-universal absence of CEFR alignment and validation reporting suggests that this second pillar is currently missing, which we term the Integrity Gap—a systematic disconnection between technological innovation and pedagogical grounding inin Intelligent Computer-Assisted Language Learning. By reframing the problem as upstream control and validation, this review informs the design of user-facing automated systems where trust, transparency, and human oversight are critical. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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23 pages, 3029 KB

Open AccessReview

Cyber–Physical Systems in Healthcare Based on Medical and Social Research Reflected in AI-Based Digital Twins of Patients

by Emilia Mikołajewska, Urszula Rogalla-Ładniak, Jolanta Masiak, Ewelina Panas and Dariusz Mikołajewski

Appl. Sci. 2026, 16(1), 318; https://doi.org/10.3390/app16010318 - 28 Dec 2025

Cited by 2 | Viewed by 871

Abstract

Cyber–physical systems (CPS) in healthcare represent a deep integration of computational intelligence, physical medical devices, and human-centric data, enabling continuous, adaptive, and personalized care. These systems combine real-time measurements, artificial intelligence (AI)-based analytics, and networked medical devices to monitor, predict, and optimize patient [...] Read more.

Cyber–physical systems (CPS) in healthcare represent a deep integration of computational intelligence, physical medical devices, and human-centric data, enabling continuous, adaptive, and personalized care. These systems combine real-time measurements, artificial intelligence (AI)-based analytics, and networked medical devices to monitor, predict, and optimize patient health outcomes. A key development in the field of CPS is the emergence of patient digital twins (DTs), virtual models of individual patients that simulate biological, behavioral, and social parameters. Using AI, DTs analyze complex medical and social data (genetics, lifestyle, environment, etc.) to support precise diagnosis and treatment planning. The implications of the bibliometric findings suggest that the field emerges from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context. Reflecting insights from medical and social research, AI-based DT systems provide a holistic view of the patient, taking into account not only physiological states but also psychological and social well-being. These systems promote personalized therapy by dynamically adapting treatment based on real-time feedback from wearable sensors and electronic medical records. More broadly, CPS and DT systems increase healthcare system efficiency by reducing hospitalizations and supporting remote preventive care. Their implementation poses significant ethical and privacy challenges, particularly regarding data ownership, algorithm transparency, and patient autonomy. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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27 pages, 6323 KB

Open AccessReview

Design of Automotive HMI: New Challenges in Enhancing User Experience, Safety, and Security

by Iwona Grobelna, David Mailland and Mikołaj Horwat

Appl. Sci. 2025, 15(10), 5572; https://doi.org/10.3390/app15105572 - 16 May 2025

Cited by 7 | Viewed by 12001

Abstract

Human–Machine Interfaces (HMIs) in traditional automobiles are essential in connecting drivers, passengers, and vehicle systems. In automated vehicles, the HMI has become a critical component. A well-designed HMI facilitates effective human oversight, enhances situational awareness, and mitigates risks associated with system failures or [...] Read more.

Human–Machine Interfaces (HMIs) in traditional automobiles are essential in connecting drivers, passengers, and vehicle systems. In automated vehicles, the HMI has become a critical component. A well-designed HMI facilitates effective human oversight, enhances situational awareness, and mitigates risks associated with system failures or unexpected scenarios. Simultaneously, it serves as a crucial safeguard against cyber threats, preventing unauthorized access and ensuring the integrity of vehicular operations in increasingly connected environments. This narrative review delves into the evolving landscape of automotive HMI design, emphasizing its role in enhancing user experience (UX) and safety. By exploring usability challenges, technological advancements, and the integration of rapidly evolving technologies such as AI (Artificial Intelligence), AR (Augmented Reality), and gesture-based controls, this study highlights how effective HMIs minimize cognitive load while maintaining functionality. Significant attention is given to the new challenges that arise from technological advancements in terms of security and safety. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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Other

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24 pages, 1666 KB

Open AccessPerspective

Additive Manufacturing for Next-Generation Batteries: Opportunities, Challenges, and Future Outlook

by Antreas Kantaros, Theodore Ganetsos, Evangelos Pallis, Michail Papoutsidakis and Nikolaos Laskaris

Appl. Sci. 2025, 15(22), 11907; https://doi.org/10.3390/app152211907 - 9 Nov 2025

Viewed by 2048

Abstract

The elevated needs for high-performance energy storage, dictated by electrification, renewable sources integration, and the global increase in interconnected devices, have placed batteries to the forefront of technological research. Additive manufacturing is increasingly recognized as a compelling approach to advance battery research and [...] Read more.

The elevated needs for high-performance energy storage, dictated by electrification, renewable sources integration, and the global increase in interconnected devices, have placed batteries to the forefront of technological research. Additive manufacturing is increasingly recognized as a compelling approach to advance battery research and application by enabling tailored control over design, pore geometry, materials, and integration. This perspective work examines the opportunities and challenges associated with utilizing additive manufacturing as an enabling battery manufacturing technology. Recent advances in the additive fabrication of electrodes, electrolytes, separators, and integrated devices are examined, exhibiting the potential to acheive electrochemical performance, design adaptability, and sustainability. At the same time, key challenges—including materials formulation, reproducibility, economic feasibility, and regulatory uncertainty—are discussed as limiting factors that must be addressed for achieving the expected results. Rather than being viewed as a replacement for conventional gigafactory-scale production, additive manufacturing is positioned as a complementary fabrication technique that can deliver value in niche, distributed, and application-specific contexts. This work concludes by outlining research and policy priorities that could accelerate the maturation of 3D-printed batteries, stressing the importance of hybrid manufacturing, multifunctional printable materials, circular economy integration, and carefully phased timelines for deployment. Moreover, by enabling customized form factors, improved device–user interfaces, and seamless integration into smart, automated environments, additive manufacturing has the potential to significantly enhance user experience across emerging battery applications. In this context, this perspective provides a grounded assessment of how additive fabrication methods may contribute to next-generation battery technologies that not only improve electrochemical performance but also enhance user interaction, reliability, and seamless integration within automated and control-driven systems. Full article

(This article belongs to the Special Issue Enhancing User Experience in Automation and Control Systems)

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