Special Issue "Intelligent Control in Industrial and Renewable Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Prof. Dr. Matilde Santos
Guest Editor
Institute of Knowledge Technology, University Complutense of Madrid, 28040 Madrid, Spain
Interests: intelligent control; modelling and simulation; soft computing; engineering applications; wind energy
Special Issues and Collections in MDPI journals
Dr. Eloy Irigoyen Gordo
Guest Editor
University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
Interests: Intelligent Control; Biomedical Engineering; Mobile Robots; Image Analysis
Prof. Dr. José Manuel Andújar Márquez
Guest Editor
Escuela Técnica Superior de Ingeniería, Universidad de Huelva, Campus de El Carmen, 21007 Huelva, Spain
Interests: Intelligent control; Renewable Energies; Education in Engineering
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Many computational intelligence and learning methods, including expert systems, fuzzy control, neural networks, genetic algorithm, artificial immune networks, swarm particle techniques, ACO, reinforcement learning, etc., have gained successful applications in many control automation fields. Intelligent Control, which is distinguished from conventional approaches since it is historically based on methodologies borrowed from Artificial Intelligence, mainly Soft Computing techniques, has been proved able to cope with problems – especially industrial ones and more recently, related to renewable energy and mobile robotics systems – where conventional methods were reputed less efficient or unsuccessful. In recent years, a trend has emerged in which techniques of computational intelligence, learning control and automation have been integrated into intelligent control or automation systems on a variety of scales to meet the needs of implementation at the angle of products.

This Special Issue is devoted to all topics related to intelligent control and its applications, including (but not limited to) the following subjects:

  • Intelligent Control: fuzzy control, neuro-control, neuro-fuzzy, intelligent-PID control, hybrid techniques, etc.
  • Optimization by heuristic techniques in system engineering and control
  • Modelling and identification by Intelligent Techniques
  • Engineering applications of Intelligent Computation
  • Applications in industry and energy system.
  • Other related topics

Prof. Dr. Matilde Santos
Dr. Eloy Irigoyen Gordo
Prof. Dr. José Manuel Andújar Márquez
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 papers will be 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 1800 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.


  • Intelligent Control
  • Soft Computing Techniques
  • Optimization
  • Modelling and Simulation
  • Engineering Applications

Published Papers (1 paper)

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Open AccessArticle
Exploring Reward Strategies for Wind Turbine Pitch Control by Reinforcement Learning
Appl. Sci. 2020, 10(21), 7462; https://doi.org/10.3390/app10217462 - 23 Oct 2020
In this work, a pitch controller of a wind turbine (WT) inspired by reinforcement learning (RL) is designed and implemented. The control system consists of a state estimator, a reward strategy, a policy table, and a policy update algorithm. Novel reward strategies related [...] Read more.
In this work, a pitch controller of a wind turbine (WT) inspired by reinforcement learning (RL) is designed and implemented. The control system consists of a state estimator, a reward strategy, a policy table, and a policy update algorithm. Novel reward strategies related to the energy deviation from the rated power are defined. They are designed to improve the efficiency of the WT. Two new categories of reward strategies are proposed: “only positive” (O-P) and “positive-negative” (P-N) rewards. The relationship of these categories with the exploration-exploitation dilemma, the use of ϵ-greedy methods and the learning convergence are also introduced and linked to the WT control problem. In addition, an extensive analysis of the influence of the different rewards in the controller performance and in the learning speed is carried out. The controller is compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. The simulations show how the P-N rewards improve the performance of the controller, stabilize the output power around the rated power, and reduce the error over time. Full article
(This article belongs to the Special Issue Intelligent Control in Industrial and Renewable Systems)
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