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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 19584

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Special Issue Editors

Prof. Dr. Eloy Irigoyen

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Guest Editor

Faculty of Engineering Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
Interests: intelligent control; biomedical engineering; mobile robots; image analysis; optimal control of H2 resources; real-time control solutions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Javier Sanchís

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Guest Editor

School of Industrial Engineering, Universitat Politècnica de València (UPV), 46022 Valencia, Spain
Interests: intelligent control; multiobjetive optimization problems; real-time control solutions; renewable energies
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Prof. Dr. Pedro Cabrera

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Guest Editor

Department of Mechanical Engineering, Universidad de Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Spain
Interests: energy-water; smart energy systems; intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In modelling and control disciplines, new and advanced proposals are continuously emerging, offering new solutions in the engineering field, with special significance in industrial applications. Taking into account the quick improvement of technology, which supports and enhances all areas of study, intelligent techniques have led to a new era where theoretical approaches may be converted into successful, reliable, and efficient real solutions.

For this reason, Intelligent Control (IC) has become a crucial branch of engineering that uninterruptedly presents new soft computing proposals of great interest in a wide range of areas such as mobile robotics, aerospace, renewable energies, biomedical engineering, and computer vision, among others, in addition to multiple industrial applications. Based on this, it is possible to develop any study to the point where the capability of new techniques converges with deep information processing through more complex algorithms and more restrictive conditions, with a big amount of uncertainty in models or with hard restrictions.

IC involves disparate fields such as neural networks (static and dynamic), evolving algorithms (genetic algorithms, swarm optimization, ant colonies, etc.), fuzzy logic (Mamdani and Takagi-Sugeno), among others, with a hybridization of all of them.

IC applied to real solutions needs to be covered in specific and delimited Special Issues with a clear profile of applicability and accuracy, reaching the highest levels of the “Technical Readiness Levels” scale.

The objectives of the Special Issue are to:

Bring together researchers and practitioners from IC and related communities;
Explore the technologies that power IC algorithms and real control platforms;
Discuss the emerging problems and open challenges identified by the IC community;
Share case studies and empirical studies of applying IC;
Discuss aspects of IC for emerging specific domains.

Topics of interest for this Special Issue include but are not limited to:

Modelling and identification using intelligent techniques.
Intelligent techniques in advanced control.
Soft computing applied to non-linear control.
Stability of intelligent control systems.
Fuzzy control: fuzzy knowledge based control (FKBC); adaptive and non-linear fuzzy control.
Hybridization of intelligent techniques in control.
Optimization by heuristic techniques in system engineering and control.
Engineering applications of intelligent computation.
Real-time developments in intelligent control.
Soft computing solutions in control.

Prof. Dr. Eloy Irigoyen
Prof. Dr. Javier Sanchís
Dr. Pedro Cabrera
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. Applied Sciences 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 2400 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

intelligent control
soft computing techniques
modelling and simulation
engineering applications
multiobjective optimization
real-time control solutions

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Published Papers (10 papers)

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Research

24 pages, 5596 KiB

Open AccessArticle

Fault-Tolerant Model Predictive Control Applied to a Sewer Network

by Antonio Cembellín, María J. Fuente, Pastora Vega and Mario Francisco

Appl. Sci. 2024, 14(12), 5359; https://doi.org/10.3390/app14125359 - 20 Jun 2024

Viewed by 749

Abstract

This paper presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The aim of this work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. For this purpose, a fault detection and diagnosis system (FDD) based on a moving window principal component analysis technique (MWPCA) will be developed to provide an online fault monitoring solution for large-scale complex processes (e.g., sewer systems) with dynamically changing characteristics, and a reconfiguration algorithm for the MPC controller taking advantage of its own features such as constraint handling. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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13 pages, 3909 KiB

Open AccessArticle

Classification of Belts Status Based on an Automatic Generator of Fuzzy Rules Base System

by Graciliano Nicolás Marichal, Ángela Hernández, Deivis Ávila and Juan Carlos García-Prada

Appl. Sci. 2024, 14(5), 1831; https://doi.org/10.3390/app14051831 - 23 Feb 2024

Viewed by 773

Abstract

The automation of maintenance is a growing field and consequently, predictive maintenance is achieving more importance. The main objective is to predict a breakage before it happens. In order to reach this, it is necessary to have an intelligent classification technique that analyzes the state of the key breakage elements and evaluates whether a replacement is necessary or not. This work presents a study to classify belts according to their state of use. For training, vibration data have been collected on a test bench using new belts, belts with half use and belts near the breaking point. The processing of these vibrations allows for extracting the characteristic parameters that can be related to its state of use, and then, after the initial analysis, these values are used as inputs for training the intelligent system. In particular, the Genetic Neuro-Fuzzy (GNF) technique has been chosen and, with the proposed algorithm, more detailed Fuzzy rules are obtained. Once the algorithm has been trained, it is possible to establish a relationship between the vibration shown by the belt and its state of use. The achieved results show that a good classifier has been built. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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16 pages, 7154 KiB

Open AccessArticle

Vision- and Lidar-Based Autonomous Docking and Recharging of a Mobile Robot for Machine Tending in Autonomous Manufacturing Environments

by Feiyu Jia, Misha Afaq, Ben Ripka, Quamrul Huda and Rafiq Ahmad

Appl. Sci. 2023, 13(19), 10675; https://doi.org/10.3390/app131910675 - 26 Sep 2023

Cited by 2 | Viewed by 3620

Abstract

Autonomous docking and recharging are among the critical tasks for autonomous mobile robots that work continuously in manufacturing environments. This requires robots to demonstrate the following abilities: (i) detecting the charging station, typically in an unstructured environment and (ii) autonomously docking to the charging station. However, the existing research, such as that on infrared range (IR) sensor-based, vision-based, and laser-based methods, identifies many difficulties and challenges, including lighting conditions, severe weather, and the need for time-consuming computation. With the development of deep learning techniques, real-time object detection methods have been widely applied in the manufacturing field for the recognition and localization of target objects. Nevertheless, those methods require a large amount of proper and high-quality data to achieve a good performance. In this study, a Hikvision camera was used to collect data from a charging station in a manufacturing environment; then, a dataset for the wireless charger was built. In addition, the authors of this paper propose an autonomous docking and recharging method based on the deep learning model and the Lidar sensor for a mobile robot operating in a manufacturing environment. In the proposed method, a YOLOv7-based object detection method was developed, trained, and evaluated to enable the robot to quickly and accurately recognize the charging station. Mobile robots can achieve autonomous docking to the charging station using the proposed Lidar-based approach. Compared to other methods, the proposed method has the potential to improve recognition accuracy and efficiency and reduce the computation costs for the mobile robot system in various manufacturing environments. The developed method was tested in real-world scenarios and achieved an average accuracy of 95% in recognizing the target charging station. This vision-based charger detection method, if fused with the proposed Lidar-based docking method, can improve the overall accuracy of the docking alignment process. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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10 pages, 2331 KiB

Open AccessCommunication

Energy Efficiency Optimization in Onboard SWRO Desalination Plants Based on a Genetic Neuro-Fuzzy System

by Ángela Hernández López, Jorge Camacho-Espino, Baltasar Peñate Suárez and Graciliano Nicolás Marichal Plasencia

Appl. Sci. 2023, 13(6), 3392; https://doi.org/10.3390/app13063392 - 7 Mar 2023

Cited by 1 | Viewed by 1686

Abstract

This work presents a novel intelligent control system based on a Genetic Neuro-Fuzzy tool to optimize and improve the performance of a seawater reverse osmosis desalination plant (SWRO) on board a marine vessel. This investigation pays special attention to minimizing energy consumption to improve the energy efficiency of this marine installation. The system analyzes measurements of different variables—seawater pH, seawater conductivity, permeate flow rate, permeate conductivity, and total energy consumed—in order to provide the most appropriate value of permeate flow rate control and operating pressure of the high-pressure pump (HPP). This intelligent method allows the plant to achieve output values nearer to the desired setpoints set by the plant operators. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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26 pages, 834 KiB

Open AccessArticle

An Emotional Model Based on Fuzzy Logic and Social Psychology for a Personal Assistant Robot

by Gema Fernández-Blanco Martín, Fernando Matía, Lucía García Gómez-Escalonilla, Daniel Galan, M. Guadalupe Sánchez-Escribano, Paloma de la Puente and Mario Rodríguez-Cantelar

Appl. Sci. 2023, 13(5), 3284; https://doi.org/10.3390/app13053284 - 4 Mar 2023

Cited by 2 | Viewed by 2452

Abstract

Personal assistants and social robotics have evolved significantly in recent years thanks to the development of artificial intelligence and affective computing. Today’s main challenge is achieving a more natural and human interaction with these systems. Integrating emotional models into social robotics is necessary to accomplish this goal. This paper presents an emotional model whose design has been supervised by psychologists, and its implementation on a social robot. Based on social psychology, this dimensional model has six dimensions with twelve emotions. Fuzzy logic has been selected for defining: (i) how the input stimuli affect the emotions and (ii) how the emotions affect the responses generated by the robot. The most significant contribution of this work is that the proposed methodology, which allows engineers to easily adapt the robot personality designed by a team of psychologists. It also allows expert psychologists to define the rules that relate the inputs and outputs to the emotions, even without technical knowledge. This methodology has been developed and validated on a personal assistant robot. It consists of three input stimuli, (i) the battery level, (ii) the brightness of the room, and (iii) the touch of caresses. In a simplified implementation of the general model, these inputs affect two emotions that generate an externalized emotional response through the robot’s heartbeat, facial expression, and tail movement. The three experiments performed verify the correct functioning of the emotional model developed, demonstrating that stimuli, independently or jointly, generate changes in emotions that, in turn, affect the robot’s responses. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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17 pages, 1054 KiB

Open AccessArticle

Deep Reinforcement Learning Agent for Negotiation in Multi-Agent Cooperative Distributed Predictive Control

by Oscar Aponte-Rengifo, Pastora Vega and Mario Francisco

Appl. Sci. 2023, 13(4), 2432; https://doi.org/10.3390/app13042432 - 14 Feb 2023

Cited by 3 | Viewed by 2193

Abstract

This paper proposes a novel solution for using deep neural networks with reinforcement learning as a valid option in negotiating distributed hierarchical controller agents. The proposed method is implemented in the upper layer of a hierarchical control architecture composed at its lowest levels by distributed control based on local models and negotiation processes with fuzzy logic. The advantage of the proposal is that it does not require the use of models in the negotiation, and it facilitates the minimization of any dynamic behavior index and the specification of constraints. Specifically, it uses a reinforcement learning policy gradient algorithm to achieve a consensus among the agents. The algorithm is successfully applied to a level system composed of eight interconnected tanks that are quite difficult to control due to their non-linear nature and the high interaction among their subsystems. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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21 pages, 4334 KiB

Open AccessArticle

Development and Evaluation of Fuzzy Logic Controllers for Improving Performance of Wind Turbines on Semi-Submersible Platforms under Different Wind Scenarios

by P. Zambrana, Javier Fernández-Quijano, Pedro M. Mayorga Rubio, J. J. Fernandez-Lozano and Alfonso García-Cerezo

Appl. Sci. 2023, 13(4), 2422; https://doi.org/10.3390/app13042422 - 13 Feb 2023

Cited by 3 | Viewed by 1652

Abstract

Among renewable energy technologies, wind energy features one of the best possibilities for large-scale integration into power systems. However, there are specific restrictions regarding the installation areas for this technology, thus resulting in a growing, yet restricted, rate of penetration of the technology because of the limited viable sites onshore or in shallow waters. In this context, the use of offshore semi-submersible platforms appears as a promising option, which additionally enables the incorporation of other elements, such as wave energy converters or aquaculture. Nevertheless, this kind of offshore facility involves interactions between platform movements and the wind turbine, increasing the complexity of the system, causing traditional control techniques to not be able to fully cope with the dynamics of the system, and thus limiting the efficiency of energy extraction. On the contrary, the use of intelligent control techniques is an interesting option to take full account of the said interactions and to improve energy capture efficiency through the control of the pitch of the blades, especially under turbulent, above-rated wind profiles. This work presents an original fuzzy logic controller that has been validated by comparing it with previously validated controllers, following a developed methodology that allows comparison of controllers for wind turbines in semi-submersible platforms using performance indexes. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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29 pages, 34121 KiB

Open AccessArticle

Kinesthetic Learning Based on Fast Marching Square Method for Manipulation

by Adrián Prados, Alicia Mora, Blanca López, Javier Muñoz, Santiago Garrido and Ramón Barber

Appl. Sci. 2023, 13(4), 2028; https://doi.org/10.3390/app13042028 - 4 Feb 2023

Cited by 7 | Viewed by 2080

Abstract

The advancement of robotics in recent years has driven the growth of robotic applications for more complex tasks requiring manipulation capabilities. Recent works have focused on adapting learning methods to manipulation applications which are stochastic and may not converge. In this paper, a kinesthetic learning method based on fast marching square is presented. This method poses great advantages such as ensuring convergence and is based on learning from the experience of a human demonstrator. For this purpose, the demonstrator teaches paths by physically guiding one of the UR3 arms of a mobile manipulator. After this first phase, the fast marching Learning method is used to make the robot learn from this experience. As a novelty, an auto-learning functionality is presented, which provides the kinesthetic learning algorithm with an exploration capacity. The base of this algorithm is not only using the information provided by the taught trajectories, but also expanding its ability in order to explore unknown states of the environment. The effectiveness of the proposed method has been evaluated through simulations in 2D and 3D environments and in a real mobile manipulator. The learning process is analyzed with other 2D learning approaches using the LASA dataset and it is tested in complex 3D scenarios with different obstacles, proving its effectiveness. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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24 pages, 17696 KiB

Open AccessArticle

Dynamic Analysis of Fuzzy Systems

by Manuel Barraza, Fernando Matía and Basil Mohammed Al-Hadithi

Appl. Sci. 2023, 13(3), 1934; https://doi.org/10.3390/app13031934 - 2 Feb 2023

Cited by 1 | Viewed by 1716

Abstract

In this work, a new methodology for the dynamic analysis of non-linear systems is developed by applying the Mamdani fuzzy model. With this model, parameters such as settling time, peak time and overshoot will be obtained. The dynamic analysis of non-linear fuzzy systems with triangular membership functions is performed, and linguistic variables describing overly complex or ill-defined phenomena are used to fit the model. Scaling factors will simplify the modification of the variables, making them easier to find the system model. The specifications of second-order characteristics in the time domain, such as overshoot and peak time, will be represented graphically. As a case study, the proposed methods are implemented to analyse the dynamics of a tank and a simple pendulum for first-order and second-order systems, respectively, where it is observed that the proposed methodology offers highly positive results. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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16 pages, 2262 KiB

Open AccessArticle

Discrete-Time Design of Dual Internal Model-Based Repetitive Control Systems

by Jalu A. Prakosa, Purwowibowo Purwowibowo, Edi Kurniawan, Sensus Wijonarko, Tatik Maftukhah, Farakka Sari, Enggar B. Pratiwi and Dadang Rustandi

Appl. Sci. 2022, 12(22), 11746; https://doi.org/10.3390/app122211746 - 18 Nov 2022

Cited by 2 | Viewed by 1548

Abstract

This paper presents a novel design of discrete-time dual internal model-based repetitive control systems. The design strategy is accomplished by combining general and high-order modified repetitive control schemes for simultaneous tracking repetitive tasks and rejection of uncertain periodic disturbances. The proposed controller is constructed from two different discrete-time internal models, rendering a dual internal model-based repetitive controller (DIMRC). The first internal model is intended to track repetitive commands with a fixed fundamental frequency. Meanwhile, the second internal model is coupled to compensate for an exogenous periodic disturbance with an uncertain frequency. The controller structure, stability conditions, and convergence analysis are discussed in this paper. The performance of the proposed controller is validated through simulation studies showing accurate tracking and excellent disturbance rejection simultaneously. Full article

(This article belongs to the Special Issue Advances in Intelligent Control and Engineering Applications)

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