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: closed (15 March 2022) | Viewed by 6364

Special Issue Editors

Prof. Dr. Matilde Santos
E-Mail Website
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, Collections and Topics in MDPI journals
Dr. Eloy Irigoyen
E-Mail Website
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. José Manuel Andújar Márquez
E-Mail Website
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, Collections and Topics 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

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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 2300 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
  • Optimization
  • Modelling and Simulation
  • Engineering Applications

Published Papers (5 papers)

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Research

Article
Control of a Variable Blade Pitch Wind Turbine Subject to Gust Wind and Actuators Saturation
Appl. Sci. 2021, 11(17), 7865; https://doi.org/10.3390/app11177865 - 26 Aug 2021
Viewed by 739
Abstract
This paper examines the dynamics and control of a variable blade pitch wind turbine during extreme gust wind and subject to actuators saturation. The mathematical model of the wind turbine is derived using the Lagrange dynamics. The controller is formulated using the Takagi–Sugeno [...] Read more.
This paper examines the dynamics and control of a variable blade pitch wind turbine during extreme gust wind and subject to actuators saturation. The mathematical model of the wind turbine is derived using the Lagrange dynamics. The controller is formulated using the Takagi–Sugeno fuzzy model and utilizes the parallel distributor compensator to obtain the feedback control gain. The controller’s objective is to obtain the generator electromagnetic torque and the blade pitch angle to attenuate the external disturbances. The (T–S) fuzzy controller with disturbances rejection properties is developed using the linear matrix inequalities technic and solved as an optimization problem. The efficacy of the proposed (T–S) fuzzy controller is illustrated via numerical simulations. Full article
(This article belongs to the Special Issue Intelligent Control in Industrial and Renewable Systems)
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Article
Multi-Objective Aerodynamic Design Optimisation Method of Fuel Cell Centrifugal Impeller Using Modified NSGA-II Algorithm
Appl. Sci. 2021, 11(16), 7659; https://doi.org/10.3390/app11167659 - 20 Aug 2021
Cited by 1 | Viewed by 889
Abstract
This paper presents a modified NSGA-II algorithm based on the spatial density (SD) operator, combined with computer graphics-based surface parameterisation methods and computational fluid dynamics (CFD) simulations. This was done to optimise the multi-objective aerodynamic design of a centrifugal impeller for a 100-kW [...] Read more.
This paper presents a modified NSGA-II algorithm based on the spatial density (SD) operator, combined with computer graphics-based surface parameterisation methods and computational fluid dynamics (CFD) simulations. This was done to optimise the multi-objective aerodynamic design of a centrifugal impeller for a 100-kW vehicle-mounted fuel cell and improve the multi-conditions aerodynamic performance of the centrifugal impeller of the vehicle-mounted fuel cell (FC). The optimisation objectives are to maximise the isentropic efficiency of the rated and common operating conditions. The optimisation results showed that the efficiency of rated working conditions had an increase of 1.29%, mass flow increase of 8.8%, pressure ratio increase of 0.74% and comprehensive margin increase of 6.2%. The efficiency of common working conditions had an increase of 1.2%, mass flow increase of 9.1%, pressure ratio increase of 0.24% and comprehensive margin increase of 10%. The optimisation effect is obvious under the premise of satisfying the constraints, which proves the optimisation method’s engineering effectiveness and provides technical support and methodological research for the multi-objective aerodynamic design optimisation of centrifugal impellers for vehicle-mounted FCs. Full article
(This article belongs to the Special Issue Intelligent Control in Industrial and Renewable Systems)
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Article
A Cluster-Based Hierarchical-Approach for the Path Planning of Swarm
Appl. Sci. 2021, 11(15), 6864; https://doi.org/10.3390/app11156864 - 26 Jul 2021
Cited by 11 | Viewed by 1037
Abstract
This paper addresses the path planning and control of multiple colonies/clusters that have unmanned aerial vehicles (UAV) which make a network in a hazardous environment. To solve the aforementioned issue, we design a new and novel hybrid algorithm. As seen in the mission [...] Read more.
This paper addresses the path planning and control of multiple colonies/clusters that have unmanned aerial vehicles (UAV) which make a network in a hazardous environment. To solve the aforementioned issue, we design a new and novel hybrid algorithm. As seen in the mission requirement, to combine the Maximum-Minimum ant colony optimization (ACO) with Vicsek based multi-agent system (MAS) to make an Artificially Intelligent (AI) scheme. In order to control and manage the different colonies, UAVs make a form of a network. The designed method overcomes the deficiencies of existing algorithms related to controlling and synchronizing the information globally. Furthermore, our designed architecture bounds, lemmatizes the pheromone, and finds the best ants which then make the most optimized path. The key contribution of this study is to merge two unique algorithms into a hybrid algorithm that has superior performance than both algorithms operating separately. Another contribution of the designed method is the ability to increase the number of individual agents inside the colony or the number of colonies with a good convergence rate. Lastly, we also compared the simulation results with the non-dominated sorting genetic algorithm II (NSGA-II) in order to prove the designed algorithm has a better convergence rate. Full article
(This article belongs to the Special Issue Intelligent Control in Industrial and Renewable Systems)
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Article
Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization
Appl. Sci. 2021, 11(6), 2844; https://doi.org/10.3390/app11062844 - 22 Mar 2021
Cited by 11 | Viewed by 1338
Abstract
This paper deals with the control problems of a wind turbine working in its nominal zone. In this region, the wind turbine speed is controlled by means of the pitch angle, which keeps the nominal power constant against wind fluctuations. The non-uniform profile [...] Read more.
This paper deals with the control problems of a wind turbine working in its nominal zone. In this region, the wind turbine speed is controlled by means of the pitch angle, which keeps the nominal power constant against wind fluctuations. The non-uniform profile of the wind causes tower displacements that must be reduced to improve the wind turbine lifetime. In this work, an adaptive control structure operating on the pitch angle variable is proposed for a nonlinear model of a wind turbine provided by FAST software. The proposed control structure is composed of a gain scheduling proportional–integral (PI) controller, an adaptive feedforward compensation for the wind speed, and an adaptive gain compensation for the tower damping. The tuning of the controller parameters is formulated as a Pareto optimization problem that minimizes the tower fore-aft displacements and the deviation of the generator speed using multi-objective genetic algorithms. Three multi-criteria decision making (MCDM) methods are compared, and a satisfactory solution is selected. The optimal solutions for power generation and for tower fore-aft displacement reduction are also obtained. The performance of these three proposed solutions is evaluated for a set of wind pattern conditions and compared with that achieved by a classical baseline PI controller. Full article
(This article belongs to the Special Issue Intelligent Control in Industrial and Renewable Systems)
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Article
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
Cited by 16 | Viewed by 1462
Abstract
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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