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Algorithms
Special Issues
Algorithmic Approaches to Control Theory and System Modeling
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Algorithmic Approaches to Control Theory and System Modeling

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 1 December 2025 | Viewed by 580

Special Issue Editors

Prof. Dr. Ramiro Barbosa

E-Mail Website
Guest Editor
Institute of Engineering of Porto, Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal
Interests: control; simulation; optimization; fractional calculus; evolutionary algorithms; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Paulo Moura Oliveira

E-Mail Website
Guest Editor
INESC-TEC, University of Trás-os-Montes e Alto Douro, 5001-911 Vila Real, Portugal
Interests: PID control; intelligent control; control engineering education; evolutionary and natural inspired metaheuristics for single and multiple objective optimisation problem solving
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Isabel Jesus

E-Mail Website
Guest Editor
Department of Electrical Engineering, Institute of Engineering-Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal
Interests: photovoltaic systems; fractional order control systems; fuzzy control systems; evolutionary algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Control theory and systems modeling analyze, design, and optimize systems that use feedback mechanisms to achieve specific goals. Classical control methods typically rely on differential equations to develop mathematical models of systems. Recently, there has been a significant increase in the use of fractional-order models using non-integer differential equations to capture more accurate system dynamics, improving control performance. Over the years, algorithmic approaches have evolved to complement classical techniques, increasing efficiency, robustness and adaptability in the control of complex systems. The latest developments in machine learning and artificial intelligence are revolutionizing thinking around control theory and system modeling.

This Special Issue invites submissions on emerging topics and innovative approaches highlighting the latest advancements in control theory and system modeling. We welcome contributions that delve into both theoretical developments and real-world applications in these fields.

Prof. Dr. Ramiro Barbosa
Dr. Paulo Moura Oliveira
Prof. Dr. Isabel Jesus
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. Algorithms is an international peer-reviewed open access monthly 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.

Keywords

  • system modeling and simulation
  • linear and nonlinear models
  • modeling of integer and fractional-order systems
  • data-driven modeling
  • reduced-order modeling
  • hybrid systems modeling
  • evolutionary algorithms
  • optimal control
  • PID control
  • model predictive control
  • fractional-order control
  • machine learning for modeling and control
  • deep learning for modeling and control
  • neural networks for modeling and control
  • robust control
  • adaptive control
  • nonlinear control
  • reinforcement learning for modeling and control
  • fuzzy logic for modeling and control
  • system identification
  • metaheuristic algorithms
  • artificial intelligence for modeling and control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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27 pages, 5382 KiB  
Article
PI-DÆ: An Adaptive PID Controller Utilizing a New Adaptive Exponent (Æ) Algorithm to Solve Derivative Term Issues
by Juan M. Barrera-Fernández, Juan Pablo Manzo Hernández, Kevin Miramontes Escobedo, Alberto Vázquez-Cervantes and Julio-César Solano-Vargas
Algorithms 2025, 18(7), 391; https://doi.org/10.3390/a18070391 - 27 Jun 2025
Viewed by 131
Abstract
This study proposes an enhanced derivative control strategy, named PI-DÆ, designed to overcome key limitations of the derivative (D) term, such as noise amplification, derivative kick (D-k), and tuning difficulties. These [...] Read more.
This study proposes an enhanced derivative control strategy, named PI-DÆ, designed to overcome key limitations of the derivative (D) term, such as noise amplification, derivative kick (D-k), and tuning difficulties. These issues often arise in high-frequency or rapidly changing systems, in which traditional PID controllers struggle. The proposed solution introduces a novel adaptive exponent algorithm (Æ) that dynamically modulates the D term based on the evolving relationship between system output and setpoint. This yields the PI-DÆ controller, which adapts in real time to changing conditions. The results show significant performance improvements. Simulation results on two systems demonstrate that PI-DÆ achieves a 90% faster response time, a 35% reduction in peak time, and a 100% improvement in settling time compared with conventional PID controllers, all while maintaining a near-zero steady-state error even under external disturbances. Unlike more-complex alternatives such as fuzzy logic, neural networks, or sliding mode control, PI-DÆ retains the simplicity and robustness of PID, avoiding high computational costs or intricate setups. This adaptive exponent strategy offers a practical and scalable enhancement to classical PID, improving performance and robustness without added complexity, and thus provides a promising control solution for real-world applications in which simplicity, adaptability, and reliability are essential. Full article
(This article belongs to the Special Issue Algorithmic Approaches to Control Theory and System Modeling)
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22 pages, 2590 KiB  
Article
Decision-Time Learning and Planning Integrated Control for the Mild Hyperbaric Chamber
by Nan Zhang, Qijing Lin and Zhuangde Jiang
Algorithms 2025, 18(7), 380; https://doi.org/10.3390/a18070380 - 23 Jun 2025
Viewed by 152
Abstract
Plateau hypoxia represents a type of hypobaric hypoxia caused by reduced atmospheric pressure at high altitudes. Pressurization therapy is one of the most effective methods for alleviating acute high-altitude sickness. This study focuses on the development of an advanced control system for a [...] Read more.
Plateau hypoxia represents a type of hypobaric hypoxia caused by reduced atmospheric pressure at high altitudes. Pressurization therapy is one of the most effective methods for alleviating acute high-altitude sickness. This study focuses on the development of an advanced control system for a vehicle-mounted mild hyperbaric chamber (MHBC) designed for the prevention and treatment of plateau hypoxia. Conventional control methods struggle to cope with the high complexity and inherent uncertainties associated with MHBC control tasks, thereby motivating the exploration of sequential decision-making approaches such as reinforcement learning. Nevertheless, the application of sequential decision-making in MHBC control encounters several challenges, including data inefficiency and non-stationary dynamics. The system’s low tolerance for trial-and-error may lead to component damage or unsafe operating conditions, and anomalies such as valve failure can emerge during long-term operation, compromising system stability. To address these challenges, this study proposes a decision-time learning and planning integrated framework for MHBC control. Specifically, an innovative latent model embedding decision-time learning is designed for system identification, separately managing system uncertainties to fine-tune the model output. Furthermore, a decision-time planning algorithm is developed and the planning process is further guided by incorporating a value network and an enhanced online policy. Experimental results demonstrate that the proposed decision-time learning and planning integrated approaches achieve notable performance in MHBC control. Full article
(This article belongs to the Special Issue Algorithmic Approaches to Control Theory and System Modeling)
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