Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
3.6
Journals
Future Internet
Special Issues
Artificial Intelligence and Control Systems for Industry 4.0 and 5.0
share announcement

Artificial Intelligence and Control Systems for Industry 4.0 and 5.0

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 6279

Special Issue Editors

Dr. Filipe Pereira

E-Mail Website
Guest Editor
1. INEGI–Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
2. MEtRICs Research Center, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
Interests: automation and industrial control; Industry 4.0; mechatronics; artificial intelligence; industrial, mobile and colaborative robots and industrial network protocols and advanced communication networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paulo Leitao

E-Mail Website
Guest Editor
1. Research Center in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
2. Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
Interests: intelligent and reconfigurable systems; cyber-physical systems; multi-agent systems; Internet of Things; distributed data analysis; factory automation; holonic systems; self-organized systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of Artificial Intelligence (AI) is continuously growing. AI is present in industrial digitalization and the transformation of industrial systems, uniting the principles of Industry 4.0 with the human-centered and sustainable vision of Industry 5.0. This Special Issue, “Artificial Intelligence and Control Systems for Industry 4.0 and 5.0”, aims to pioneer the way AI-based technologies will be presented, enabling smarter, more adaptable and efficient industrial systems.

In Industry 4.0/5.0, AI enhances real-time data processing, predictive maintenance and interconnected smart manufacturing systems. As Industry 5.0 emerges, AI facilitates deeper collaboration between humans and robots, promotes sustainability and improves decision-making processes. Along with advances in automation, robotics, edge and cloud computing, 5G/6G networks and augmented reality, AI is driving innovations in cyber-physical systems, industrial communication protocols and self-organizing networks. These technologies enable safety and adaptability in industrial environments to a greater extent.

This Special Issue explores the fundamental role of AI in enabling next-generation industrial systems, covering topics such as intelligent robotics, adaptive automation, distributed control, evolutionary computing and secure communications networks. Contributions focusing on the integration of AI in these domains are particularly welcome, as they highlight the potential of AI in Industry 4.0/5.0.

We encourage submissions about the following subjects:

  • AI-based control systems for smart and sustainable production;
  • Integration of edge and cloud computing, 5G/6G networks and augmented reality in industrial environments;
  • Intelligent automation, collaborative robotics and cyber-physical systems (CPS);
  • Advanced industrial communication and cybersecurity protocols;
  • Evolutionary and natural computing applications in adaptive and self-organizing systems.

Dr. Filipe Pereira
Prof. Dr. Paulo Leitao
Guest Editors

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. Future Internet 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 1600 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

  • Industry 4.0
  • Industry 5.0
  • artificial intelligence
  • control systems
  • edge computing
  • cloud computing
  • augmented reality
  • 5G networks
  • 6G networks
  • cybersecurity
  • intelligent automation
  • collaborative robotics
  • cyber-physical systems (CPS)
  • adaptive and autonomous systems
  • reconfigurable manufacturing
  • multi-agent systems
  • holonic systems
  • evolutionary computation
  • natural computing
  • IOT and smart factories
  • distributed control systems
  • advanced industrial communication protocols (OPC-UA, MQTT, EtherCAT, profinet, Ethernet-IP and modbus TCP-IP)
  • predictive maintenance in Industry 4.0/5.0
  • factory automation
  • human–robot collaboration

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 3251 KB  
Article
Intelligent Control Approaches for Warehouse Performance Optimisation in Industry 4.0 Using Machine Learning
by Ádám Francuz and Tamás Bányai
Future Internet 2025, 17(10), 468; https://doi.org/10.3390/fi17100468 - 11 Oct 2025
Viewed by 303
Abstract
In conventional logistics optimization problems, an objective function describes the relationship between parameters. However, in many industrial practices, such a relationship is unknown, and only observational data is available. The objective of the research is to use machine learning-based regression models to uncover [...] Read more.
In conventional logistics optimization problems, an objective function describes the relationship between parameters. However, in many industrial practices, such a relationship is unknown, and only observational data is available. The objective of the research is to use machine learning-based regression models to uncover patterns in the warehousing dataset and use them to generate an accurate objective function. The models are not only suitable for prediction, but also for interpreting the effect of input variables. This data-driven approach is consistent with the automated, intelligent systems of Industry 4.0, while Industry 5.0 provides opportunities for sustainable, flexible, and collaborative development. In this research, machine learning (ML) models were tested on a fictional dataset using Automated Machine Learning (AutoML), through which Light Gradient Boosting Machine (LightGBM) was selected as the best method (R2 = 0.994). Feature Importance and Partial Dependence Plots revealed the key factors influencing storage performance and their functional relationships. Defining performance as a cost indicator allowed us to interpret optimization as cost minimization, demonstrating that ML-based methods can uncover hidden patterns and support efficiency improvements in warehousing. The proposed approach not only achieves outstanding predictive accuracy, but also transforms model outputs into actionable, interpretable insights for warehouse optimization. By combining automation, interpretability, and optimization, this research advances the practical realization of intelligent warehouse systems in the era of Industry 4.0. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)
►▼ Show Figures

Figure 1

27 pages, 1588 KB  
Article
Toward the Theoretical Foundations of Industry 6.0: A Framework for AI-Driven Decentralized Manufacturing Control
by Andrés Fernández-Miguel, Susana Ortíz-Marcos, Mariano Jiménez-Calzado, Alfonso P. Fernández del Hoyo, Fernando E. García-Muiña and Davide Settembre-Blundo
Future Internet 2025, 17(10), 455; https://doi.org/10.3390/fi17100455 - 3 Oct 2025
Viewed by 534
Abstract
This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyber–physical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and [...] Read more.
This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyber–physical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)
►▼ Show Figures

Figure 1

27 pages, 4067 KB  
Article
Opportunities for Adapting Data Write Latency in Geo-Distributed Replicas of Multicloud Systems
by Olha Kozina, José Machado, Maksym Volk, Hennadii Heiko, Volodymyr Panchenko, Mykyta Kozin and Maryna Ivanova
Future Internet 2025, 17(10), 442; https://doi.org/10.3390/fi17100442 - 28 Sep 2025
Viewed by 270
Abstract
This paper proposes an AI-based approach to adapting the data write latency in multicloud systems (MCSs) that supports data consistency across geo-distributed replicas of cloud service providers (CSPs). The proposed approach allows for dynamically forming adaptation scenarios based on the proposed model of [...] Read more.
This paper proposes an AI-based approach to adapting the data write latency in multicloud systems (MCSs) that supports data consistency across geo-distributed replicas of cloud service providers (CSPs). The proposed approach allows for dynamically forming adaptation scenarios based on the proposed model of multi-criteria optimization of data write latency. The generated adaptation scenarios are aimed at maintaining the required data write latency under changes in the intensity of the incoming request flow and network transmission time between replicas in CSPs. To generate adaptation scenarios, the features of the algorithmic Latord method of data consistency, are used. To determine the threshold values and predict the external parameters affecting the data write latency, we propose using learning AI models. An artificial neural network is used to form rules for changing the parameters of the Latord method when the external operating conditions of MCSs change. The features of the Latord method that influence data write latency are demonstrated by the results of simulation experiments on three MCSs with different configurations. To confirm the effectiveness of the developed approach, an adaptation scenario was considered that allows reducing the data write latency by 13% when changing the standard deviation of network transmission time between DCs of MCS. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)
►▼ Show Figures

Figure 1

18 pages, 812 KB  
Article
Deep Reinforcement Learning for Adaptive Robotic Grasping and Post-Grasp Manipulation in Simulated Dynamic Environments
by Henrique C. Ferreira and Ramiro S. Barbosa
Future Internet 2025, 17(10), 437; https://doi.org/10.3390/fi17100437 - 26 Sep 2025
Viewed by 565
Abstract
This article presents a deep reinforcement learning (DRL) approach for adaptive robotic grasping in dynamic environments. We developed UR5GraspingEnv, a PyBullet-based simulation environment integrated with OpenAI Gym, to train a UR5 robotic arm with a Robotiq 2F-85 gripper. Soft Actor-Critic (SAC) and Proximal [...] Read more.
This article presents a deep reinforcement learning (DRL) approach for adaptive robotic grasping in dynamic environments. We developed UR5GraspingEnv, a PyBullet-based simulation environment integrated with OpenAI Gym, to train a UR5 robotic arm with a Robotiq 2F-85 gripper. Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) were implemented to learn robust grasping policies for randomly positioned objects. A tailored reward function, combining distance penalties, grasp, and pose rewards, optimizes grasping and post-grasping tasks, enhanced by domain randomization. SAC achieves an 87% grasp success rate and 75% post-grasp success, outperforming PPO 82% and 68%, with stable convergence over 100,000 timesteps. The system addresses post-grasping manipulation and sim-to-real transfer challenges, advancing industrial and assistive applications. Results demonstrate the feasibility of learning stable and goal-driven policies for single-arm robotic manipulation using minimal supervision. Both PPO and SAC yield competitive performance, with SAC exhibiting superior adaptability in cluttered or edge cases. These findings suggest that DRL, when carefully designed and monitored, can support scalable learning in manipulation tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)
►▼ Show Figures

Figure 1

22 pages, 2036 KB  
Article
AI-Driven Transformations in Manufacturing: Bridging Industry 4.0, 5.0, and 6.0 in Sustainable Value Chains
by Andrés Fernández-Miguel, Fernando Enrique García-Muiña, Susana Ortíz-Marcos, Mariano Jiménez-Calzado, Alfonso P. Fernández del Hoyo and Davide Settembre-Blundo
Future Internet 2025, 17(9), 430; https://doi.org/10.3390/fi17090430 - 21 Sep 2025
Viewed by 805
Abstract
This study investigates how AI-driven innovations are reshaping manufacturing value chains through the transition from Industry 4.0 to Industry 6.0, particularly in resource-intensive sectors such as ceramics. Addressing a gap in the literature, the research situates the evolution of manufacturing within the broader [...] Read more.
This study investigates how AI-driven innovations are reshaping manufacturing value chains through the transition from Industry 4.0 to Industry 6.0, particularly in resource-intensive sectors such as ceramics. Addressing a gap in the literature, the research situates the evolution of manufacturing within the broader context of digital transformation, sustainability, and regulatory demands. A mixed-methods approach was employed, combining semi-structured interviews with key industry stakeholders and an extensive review of secondary data, to develop an Industry 6.0 model tailored to the ceramics industry. The findings demonstrate that artificial intelligence, digital twins, and cognitive automation significantly enhance predictive maintenance, real-time supply chain optimization, and regulatory compliance, notably with the Corporate Sustainability Reporting Directive (CSRD). These technological advancements also facilitate circular economy practices and cognitive logistics, thereby fostering greater transparency and sustainability in B2B manufacturing networks. The study concludes that integrating AI-driven automation and cognitive logistics into digital ecosystems and supply chain management serves as a strategic enabler of operational resilience, regulatory alignment, and long-term competitiveness. While the industry-specific focus may limit generalizability, the study underscores the need for further research in diverse manufacturing sectors and longitudinal analyses to fully assess the long-term impact of AI-enabled Industry 6.0 frameworks. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)
►▼ Show Figures

Figure 1

26 pages, 1131 KB  
Article
The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
by Lucas Sakurada, Fernando De la Prieta and Paulo Leitao
Future Internet 2025, 17(7), 270; https://doi.org/10.3390/fi17070270 - 20 Jun 2025
Cited by 4 | Viewed by 1632
Abstract
In the context of Industry 4.0 (I4.0), the Asset Administration Shell (AAS) has been gaining significant attention in recent years. The AAS serves as a standardized digital representation of an asset, encapsulating all relevant information about the asset throughout its lifecycle. Since its [...] Read more.
In the context of Industry 4.0 (I4.0), the Asset Administration Shell (AAS) has been gaining significant attention in recent years. The AAS serves as a standardized digital representation of an asset, encapsulating all relevant information about the asset throughout its lifecycle. Since its introduction in 2015, the past decade has seen considerable progress in developing traditional AAS solutions, namely AAS Type 1 and Type 2. As this initial phase reaches maturity, it becomes essential to shift focus toward AAS Type 3 (proactive), a specific category that extends traditional AAS functionalities by incorporating higher levels of autonomy, intelligence, and collaborative capabilities. However, AAS Type 3 is still in its early stages, lacking formal specifications and comprehensive implementation guidelines. In this context, Multi-Agent Systems (MAS) have been investigated as a means to enhance traditional AAS solutions toward the realization of AAS Type 3, particularly by embedding autonomous, intelligent, and collaborative behaviors. Building on this perspective, this paper explores the role of MAS in realizing AAS Type 3 through a comprehensive analysis of existing agent-based AAS approaches in the literature. Furthermore, this paper proposes a reference model based on common patterns found in the literature to support the development of AAS Type 3 solutions, contributing to the discussion on the formalization of specifications and providing greater clarity on this emerging topic. Finally, to better demonstrate key aspects of the model, some illustrative examples are presented to guide its application and facilitate understanding. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)
►▼ Show Figures

Figure 1

17 pages, 12426 KB  
Article
Implementation and Performance Analysis of an Industrial Robot’s Vision System Based on Cloud Vision Services
by Ioana-Livia Stefan, Andrei Mateescu, Ionut Lentoiu, Silviu Raileanu, Florin Daniel Anton, Dragos Constantin Popescu and Ioan Stefan Sacala
Future Internet 2025, 17(5), 200; https://doi.org/10.3390/fi17050200 - 30 Apr 2025
Cited by 1 | Viewed by 831
Abstract
With its fast advancements, cloud computing opens many opportunities for research in various applications from the robotics field. In our paper, we further explore the prospect of integrating Cloud AI object recognition services into an industrial robotics sorting task. Starting from our previously [...] Read more.
With its fast advancements, cloud computing opens many opportunities for research in various applications from the robotics field. In our paper, we further explore the prospect of integrating Cloud AI object recognition services into an industrial robotics sorting task. Starting from our previously implemented solution on a digital twin, we are now putting our proposed architecture to the test in the real world, on an industrial robot, where factors such as illumination, shadows, different colors, and textures of the materials influence the performance of the vision system. We compare the results of our suggested method with those from an industrial machine vision software, indicating promising performance and opening additional application perspectives in the robotics field, simultaneously with the continuous improvement of Cloud and AI technology. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop