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Automation
Special Issues
Networked Predictive Control for Complex Systems
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Networked Predictive Control for Complex Systems

A special issue of Automation (ISSN 2673-4052).

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 16263

Special Issue Editors

Prof. Dr. Nicola Epicoco

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Guest Editor
Department of Information Engineering, Computer Science and Mathematics (DISIM), Center of Excellence DEWS (Design Methodologies for Embedded Controllers, Wireless Interconnect and System–on–Chip), University of L’Aquila (Italy), via Vetoio, 67100 L’Aquila, Italy
Interests: ecision and control; optimization; dynamic systems; fuzzy set theory
Prof. Dr. Raffaele Carli

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering, Polytechnic of Bari, 70126 Bari, Italy
Interests: optimization; modeling and simulation; game theory; control theory; energy and power; home and building; industry 4.0; logistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Graziana Cavone

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering (DEI), Polytechnic of Bari, Via Orabona 4, 70125 Bari, Italy
Interests: intelligent transportation; logistics; energy management; resilient energy systems; automation and control theory
Dr. Domenico Bianchi

E-Mail Website
Guest Editor
Department of Information Engineering, Computer Science and Mathematics (DISIM), University of L'Aquila, Via Vetoio, 67100 L’Aquila, Italy
Interests: control of multiagents; networked and distributed systems; nonlinear systems; optimal control, automotive drones
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution and diffusion of information and communication technologies are leading to the implementation of complex networked systems that rely on communication, computation, and control techniques for their proper functioning.

In this context, researchers are interested in developing novel control methods for  network-centric complex systems. The concept of networked control systems has thus been introduced, which refers to a distributed real-time feedback control system that integrates sensors, controllers, actuators and communication networks. In this perspective, the control of networked systems implies that the network used for the communication of the control actions is general purpose and used for various simultaneous applications; moreover, the functionalities of the control level must be diversified from the pure automatic control. Consequently, it becomes challenging to ensure real-time communications in the whole system and to guarantee high performance and stability.

Predictive control methods, thanks to the possibility of predicting the systems behavior, explicitly restrict its functioning, and optimize its performance, and are considered promising in the context of networked control systems.

The objective of this Special Issue is to collect recent research and development efforts contributing to advances in networked predictive control systems, also including state-of-the-art reviews and perspectives on future advances and applications.

Prof. Dr. Raffaele Carli
Prof. Dr. Graziana Cavone
Prof. Dr. Nicola Epicoco
Dr. Domenico Bianchi
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. Automation is an international peer-reviewed open access quarterly 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 1000 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

  • predictive control
  • networked systems
  • optimal control
  • distributed control
  • agent-based control

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Published Papers (4 papers)

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Research

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14 pages, 862 KiB  
Article
Can Artificial Neural Networks Be Used to Predict Bitcoin Data?
by Terje Solsvik Kristensen and Asgeir H. Sognefest
Automation 2023, 4(3), 232-245; https://doi.org/10.3390/automation4030014 - 25 Aug 2023
Viewed by 1877
Abstract
Financial markets are complex, evolving dynamic systems. Due to their irregularity, financial time series forecasting is regarded as a rather challenging task. In recent years, artificial neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have [...] Read more.
Financial markets are complex, evolving dynamic systems. Due to their irregularity, financial time series forecasting is regarded as a rather challenging task. In recent years, artificial neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. The objective of this paper is to present this versatile framework and attempt to use it to predict the stock return series of four public-listed companies on the New York Stock Exchange. Our findings coincide with those of Burton Malkiel in his book, A Random Walk Down Wall Street; no conclusive evidence is found that our proposed models can predict the stock return series better than that of a random walk. Full article
(This article belongs to the Special Issue Networked Predictive Control for Complex Systems)
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26 pages, 5075 KiB  
Article
Optimal Dynamic Control of Proxy War Arms Support
by Peter Lohmander
Automation 2023, 4(1), 31-56; https://doi.org/10.3390/automation4010004 - 30 Jan 2023
Cited by 1 | Viewed by 3658
Abstract
A proxy war between a coalition of countries, BLUE, and a country, RED, is considered. RED wants to increase the size of the RED territory. BLUE wants to involve more regions in trade and other types of cooperation. GREEN is a small and [...] Read more.
A proxy war between a coalition of countries, BLUE, and a country, RED, is considered. RED wants to increase the size of the RED territory. BLUE wants to involve more regions in trade and other types of cooperation. GREEN is a small and independent nation that wants to become a member of BLUE. RED attacks GREEN and tries to invade. BLUE decides to give optimal arms support to GREEN. This support can help GREEN in the war against RED and simultaneously can reduce the military power of RED, which is valuable to BLUE also outside this proxy war, since RED may confront BLUE also in other regions. The optimal control problem of dynamic arms support, from the BLUE perspective, is defined in general form. From the BLUE perspective, there is an optimal position of the front. This position is a function of the weights in the objective function and all other parameters. Optimal control theory is used to determine the optimal dynamic BLUE strategy, conditional on a RED strategy, which is observed by BLUE military intelligence. The optimal arms support strategy for BLUE is to initially send a large volume of arms support to GREEN, to rapidly move the front to the optimal position. Then, the support should be almost constant during most of the war, keeping the war front location stationary. In the final part of the conflict, when RED will have almost no military resources left and tries to retire from the GREEN territory, BLUE should strongly increase the arms support and make sure that GREEN rapidly can regain the complete territory and end the war. Full article
(This article belongs to the Special Issue Networked Predictive Control for Complex Systems)
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14 pages, 3299 KiB  
Article
Colored 3D Path Extraction Based on Depth-RGB Sensor for Welding Robot Trajectory Generation
by Alfonso Gómez-Espinosa, Jesús B. Rodríguez-Suárez, Enrique Cuan-Urquizo, Jesús Arturo Escobedo Cabello and Rick L. Swenson
Automation 2021, 2(4), 252-265; https://doi.org/10.3390/automation2040016 - 5 Nov 2021
Cited by 10 | Viewed by 4619
Abstract
The necessity for intelligent welding robots that meet the demand in real industrial production, according to the objectives of Industry 4.0, has been supported owing to the rapid development of computer vision and the use of new technologies. To improve the efficiency in [...] Read more.
The necessity for intelligent welding robots that meet the demand in real industrial production, according to the objectives of Industry 4.0, has been supported owing to the rapid development of computer vision and the use of new technologies. To improve the efficiency in weld location for industrial robots, this work focuses on trajectory extraction based on color features identification on three-dimensional surfaces acquired with a depth-RGB sensor. The system is planned to be used with a low-cost Intel RealSense D435 sensor for the reconstruction of 3D models based on stereo vision and the built-in color sensor to quickly identify the objective trajectory, since the parts to be welded are previously marked with different colors, indicating the locations of the welding trajectories to be followed. This work focuses on 3D color segmentation with which the points of the target trajectory are segmented by color thresholds in HSV color space and a spline cubic interpolation algorithm is implemented to obtain a smooth trajectory. Experimental results have shown that the RMSE error for V-type butt joint path extraction was under 1.1 mm and below 0.6 mm for a straight butt joint; in addition, the system seems to be suitable for welding beads of various shapes. Full article
(This article belongs to the Special Issue Networked Predictive Control for Complex Systems)
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Review

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21 pages, 1621 KiB  
Review
Engineering Emergence: A Survey on Control in the World of Complex Networks
by Cristian Berceanu and Monica Pătrașcu
Automation 2022, 3(1), 176-196; https://doi.org/10.3390/automation3010009 - 10 Mar 2022
Cited by 6 | Viewed by 4079 | Correction
Abstract
Complex networks make an enticing research topic that has been increasingly attracting researchers from control systems and various other domains over the last two decades. The aim of this paper was to survey the interest in control related to complex networks research over [...] Read more.
Complex networks make an enticing research topic that has been increasingly attracting researchers from control systems and various other domains over the last two decades. The aim of this paper was to survey the interest in control related to complex networks research over time since 2000 and to identify recent trends that may generate new research directions. The survey was performed for Web of Science, Scopus, and IEEEXplore publications related to complex networks. Based on our findings, we raised several questions and highlighted ongoing interests in the control of complex networks. Full article
(This article belongs to the Special Issue Networked Predictive Control for Complex Systems)
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