Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

ICSTCC 2022: Advances in Monitoring and Control

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 18087

Share This Special Issue

Special Issue Editors

Prof. Dr. Marian Barbu

E-Mail Website
Guest Editor

Department of Automation and Electrical Engineering, Dunarea de Jos University of Galati, Galati, Romania
Interests: wastewater control systems; control of integrated water systems; data-driven control; application to environmental systems; application to energy systems
Special Issues, Collections and Topics in MDPI journals

Dr. Răzvan Şolea

E-Mail Website
Guest Editor

Department of Automatic Control and Electrical Engineering, ”Dunarea de Jos” University of Galati, 800008 Galati, Romania
Interests: nonlinear control systems; collaborative robotics; mobile robot design and control, machine vision and factory automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will include selected papers from the 26^th International Conference on System Theory, Control and Computing (ICSTCC 2022), to be held in Sinaia, Romania, 19–21 October 2022.

The International Conference on System Theory, Control and Computing—ICSTCC 2022—aims at bringing together, in a unique forum, scientists from academia and industry to discuss the state of the art and new trends in system theory, control and computer engineering, as well as at promoting professional interactions and fellowships.

The papers considered for ICSTCC 2022 cover the general areas of automation and robotics; computer science and engineering; and electronics and instrumentation.

The authors of selected papers from ICSTCC 2022 will be invited to submit extended and enhanced versions of their papers to this Special Issue.

Prof. Dr. Marian Barbu
Dr. Răzvan Şolea
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. Sensors is an international peer-reviewed open access semimonthly 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.

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 (3 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

29 pages, 36648 KB

Open AccessArticle

Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent

by Marcel Nicola, Claudiu-Ionel Nicola, Cosmin Ionete, Dorin Șendrescu and Monica Roman

Sensors 2023, 23(13), 5799; https://doi.org/10.3390/s23135799 - 21 Jun 2023

Cited by 10 | Viewed by 2606

Abstract

This paper summarizes a robust controller based on the fact that, in the operation of a permanent magnet synchronous motor (PMSM), a number of disturbance factors naturally occur, among which both changes in internal parameters (e.g., stator resistance R_s and combined inertia of rotor and load J) and changes in load torque T_L can be mentioned. In this way, the performance of the control system can be maintained over a relatively wide range of variation in the types of parameters mentioned above. It also presents the synthesis of robust control, the implementation in MATLAB/Simulink, and an improved version using a reinforcement learning twin-delayed deep deterministic policy gradient (RL-TD3) agent, working in tandem with the robust controller to achieve superior performance of the PMSM sensored control system. The comparison of the proposed control systems, in the case of sensored control versus the classical field oriented control (FOC) structure, based on classical PI-type controllers, is made both in terms of the usual response time and error speed ripple, but also in terms of the fractal dimension (DF) of the rotor speed signal, by verifying the hypothesis that the use of a more efficient control system results in a higher DF of the controlled variable. Starting from a basic structure of an ESO-type observer which, by its structure, allows the estimation of both the PMSM rotor speed and a term incorporating the disturbances on the system (from which, in this case, an estimate of the PMSM load torque can be extracted), four variants of observers are proposed, obtained by combining the use of a multiple neural network (NN) load torque observer and an RL-TD3 agent. The numerical simulations performed in MATLAB/Simulink validate the superior performance obtained by using properly trained RL-TD3 agents, both in the case of sensored and sensorless control. Full article

(This article belongs to the Special Issue ICSTCC 2022: Advances in Monitoring and Control)

► Show Figures

Figure 1

23 pages, 9022 KB

Open AccessArticle

Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models

by Dan Munteanu, Diana Moina, Cristina Gabriela Zamfir, Ștefan Mihai Petrea, Dragos Sebastian Cristea and Nicoleta Munteanu

Sensors 2022, 22(23), 9536; https://doi.org/10.3390/s22239536 - 6 Dec 2022

Cited by 33 | Viewed by 9365

Abstract

In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras. Full article

(This article belongs to the Special Issue ICSTCC 2022: Advances in Monitoring and Control)

► Show Figures

Figure 1

35 pages, 11716 KB

Open AccessArticle

Digital Twin for a Multifunctional Technology of Flexible Assembly on a Mechatronics Line with Integrated Robotic Systems and Mobile Visual Sensor—Challenges towards Industry 5.0

by Eugenia Mincă, Adrian Filipescu, Daniela Cernega, Răzvan Șolea, Adriana Filipescu, Dan Ionescu and Georgian Simion

Sensors 2022, 22(21), 8153; https://doi.org/10.3390/s22218153 - 25 Oct 2022

Cited by 36 | Viewed by 4599

Abstract

A digital twin for a multifunctional technology for flexible manufacturing on an assembly, disassembly, and repair mechatronics line (A/D/RML), assisted by a complex autonomous system (CAS), is presented in the paper. The hardware architecture consists of the A/D/RML and a six-workstation (WS) mechatronics line (ML) connected to a flexible cell (FC) and equipped with a six-degree of freedom (DOF) industrial robotic manipulator (IRM). The CAS has in its structure two driving wheels and one free wheel (2DW/1FW)-wheeled mobile robot (WMR) equipped with a 7-DOF robotic manipulator (RM). On the end effector of the RM, a mobile visual servoing system (eye-in-hand MVSS) is mounted. The multifunctionality is provided by the three actions, assembly, disassembly, and repair, while the flexibility is due to the assembly of different products. After disassembly or repair, CAS picks up the disassembled components and transports them to the appropriate storage depots for reuse. Disassembling or repairing starts after assembling, and the final assembled product fails the quality test. The virtual world that serves as the digital counterpart consists of tasks assignment, planning and synchronization of A/D/RML with integrated robotic systems, IRM, and CAS. Additionally, the virtual world includes hybrid modeling with synchronized hybrid Petri nets (SHPN), simulation of the SHPN models, modeling of the MVSS, and simulation of the trajectory-tracking sliding-mode control (TTSMC) of the CAS. The real world, as counterpart of the digital twin, consists of communication, synchronization, and control of A/D/RML and CAS. In addition, the real world includes control of the MVSS, the inverse kinematic control (IKC) of the RM and graphic user interface (GUI) for monitoring and real-time control of the whole system. The “Digital twin” approach has been designed to meet all the requirements and attributes of Industry 4.0 and beyond towards Industry 5.0, the target being a closer collaboration between the human operator and the production line. Full article

(This article belongs to the Special Issue ICSTCC 2022: Advances in Monitoring and Control)

► Show Figures

Journal Menu

Journal Browser

ICSTCC 2022: Advances in Monitoring and Control

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (3 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI