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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 9419

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Editors

Prof. Dr. Ramiro Barbosa

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Guest Editor

Electrical Engineering, Instituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, 4249-015 Porto, Portugal
Interests: control; modeling; simulation; artificial intelligence; fractional calculus; fractional-order control; evolutionary algorithms; robotics
Special Issues, Collections and Topics in MDPI journals

Dr. Paulo Moura Oliveira

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Guest Editor

Department of Engineering, University of Trás‑os‑Montes and Alto Douro (UTAD), INESC-TEC Research Centre, 5000‑801 Vila Real, Portugal
Interests: control engineering; PID control; control engineering education; robotics; artificial intelligence; evolutionary computation; machine learning
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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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Published Papers (7 papers)

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24 pages, 4512 KB

Open AccessArticle

Optimization of the Controller Settings for the Mean Arterial Blood Pressure Regulation Using Pelican Optimization Approach

by Abhishek Jain, Mohammad Atif Siddiqui, Tirumalasetty Chiranjeevi and Łukasz Knypiński

Algorithms 2026, 19(7), 565; https://doi.org/10.3390/a19070565 - 9 Jul 2026

Viewed by 168

Abstract

This paper presents a unified comparative study of various controllers, including proportional–integral–derivative (PID), Fractional-Order PID (FOPID), Internal Model Control (IMC) controllers, and Tilt–Integral–Derivative (TID) controllers, for the regulation of mean arterial blood pressure (MABP). The controllers are optimally tuned by using a single metaheuristic approach, namely the Pelican Optimization Algorithm (POA), ensuring a fair and consistent comparison. The POA optimizes the objective function using standard error indices (ITAE, IAE, and ISE) along with transient characteristics. The aforementioned controllers are then evaluated under varying patient conditions for different patient categories, including sensitive, nominal, and insensitive, and their performance is systematically compared with one another and with the reported methods from the existing literature. The simulation results demonstrate that IMC offers fast settling with minimal overshoot, FOPID improves robustness through fractional dynamics, and the TID controller provides the smoothest transient response and disturbance rejection across all patient categories. The results confirm the effectiveness of advanced control strategies over conventional PID and highlight the potential of POA-tuned TID control for reliable and patient-specific MABP regulation in critical care applications. Full article

(This article belongs to the Special Issue Algorithmic Approaches to Control Theory and System Modeling)

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32 pages, 6188 KB

Open AccessArticle

Performance Enhancement of Quadrotor UAVs via Gray Wolf Optimized Algorithm for Sliding Mode Control

by Mustafa B. Nidham, Khalid Yahya, Mehdi Safaei, Nawal Rai and Saleh Al Dawsari

Algorithms 2026, 19(5), 331; https://doi.org/10.3390/a19050331 - 24 Apr 2026

Viewed by 519

Abstract

This article is an in-depth analysis of the performance and efficiency of various control systems used in quadrotor unmanned aerial vehicles (UAVs). The study is focused on the comparison of three main control approaches, including Sliding Mode Control (SMC), Fuzzy Logic Control (FLC), and an extended version of Sliding Mode Control with the use of the Gray Wolf Optimizer (SMC-GWO), as well as a supportive validation model the Genetic Algorithm (SMC-GA). Based on the Newton–Euler formulation, the mathematical model of a quadrotor has been developed to provide a true picture of the dynamic behavior of the quadrotor. The model was then implemented in MATLAB/Simulink 2025b to test the performance of the system in its nominal and perturbed conditions. The findings have shown that the hybrid SMC-GWO controller has significant improvement in response speed, accuracy, and stability compared to the other controllers. Precisely, the SMC-GWO demonstrated 78.46 percent decrease in rise time and 23.40 percent decrease in settling time compared to the traditional SMC, as well as a nearly negligible steady-state error (SSE = 0.0008) in the roll channel. The proposed controller in the pitch channel reduced the rise time by 93.65 percent and the settling time by 20.22 percent, with a much smoother and more stable tracking and an effectively negligible steady-state error (SSE = 0.0001). The hybrid controller in the yaw channel had a 77.94 percent better rise time and 23.16 percent better settling time, resulting in a steady-state error of 0.0022. In relation to altitude control, SMC-GWO decreased the rise time by 91.87 percent and settling time by 25.04 percent over classical SMC, yet the steady-state error was almost zero. Under constant, time-varying actuator disturbances, the SMC-GWO controller also demonstrated better system stabilization and trajectory-tracking behavior than both SMC and FLC, as well as slightly better behavior than SMC-GA in the presence of faults and disturbances. These results verify that a UAV control framework based on the combination of the Gray Wolf Optimizer and Sliding Mode Control is more resilient, quick, and significantly more precise. Full article

(This article belongs to the Special Issue Algorithmic Approaches to Control Theory and System Modeling)

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27 pages, 3210 KB

Open AccessArticle

A Robust Lyapunov-Based Control Strategy for DC–DC Boost Converters

by Mario Ivan Nava-Bustamante, José Luis Meza-Medina, Rodrigo Loera-Palomo, Cesar Alberto Hernández-Jacobo and Jorge Alberto Morales-Saldaña

Algorithms 2025, 18(11), 705; https://doi.org/10.3390/a18110705 - 5 Nov 2025

Cited by 2 | Viewed by 1078

Abstract

This paper presents a robust and reliable voltage regulation method in DC–DC converters, for which a multiloop control strategy is developed and analyzed for a boost converter. The proposed control scheme consists of an inner current loop and an outer voltage loop, both systematically designed using the control Lyapunov function (CLF) methodology. The main contributions of this work are (1) the formulation of a control structure capable of maintaining performance under variations in load, reference voltage, and input voltage; (2) the theoretical demonstration of global asymptotic stability of the closed-loop system in the Lyapunov sense; and (3) the experimental validation of the proposed controller on a physical DC–DC boost converter, confirming its effectiveness. The results support the advancement of high-efficiency nonlinear control methods for power electronics applications. Furthermore, the experimental findings reinforce the practical relevance and real-world applicability of the proposed approach. Full article

(This article belongs to the Special Issue Algorithmic Approaches to Control Theory and System Modeling)

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28 pages, 5869 KB

Open AccessEditor’s ChoiceArticle

Comparison of Classical and Artificial Intelligence Algorithms to the Optimization of Photovoltaic Panels Using MPPT

by João T. Sousa and Ramiro S. Barbosa

Algorithms 2025, 18(8), 493; https://doi.org/10.3390/a18080493 - 7 Aug 2025

Cited by 6 | Viewed by 2013

Abstract

This work investigates the application of artificial intelligence techniques for optimizing photovoltaic systems using maximum power point tracking (MPPT) algorithms. Simulation models were developed in MATLAB/Simulink (Version 2024), incorporating conventional and intelligent control strategies such as fuzzy logic, genetic algorithms, neural networks, and Deep Reinforcement Learning. A DC/DC buck converter was designed and tested under various irradiance and temperature profiles, including scenarios with partial shading conditions. The performance of the implemented MPPT algorithms was evaluated using such metrics as Mean Absolute Error (MAE), Integral Absolute Error (IAE), mean squared error (MSE), Integral Squared Error (ISE), efficiency, and convergence time. The results highlight that AI-based methods, particularly neural networks and Deep Q-Network agents, outperform traditional approaches, especially in non-uniform operating conditions. These findings demonstrate the potential of intelligent controllers to enhance the energy harvesting capability of photovoltaic systems. Full article

(This article belongs to the Special Issue Algorithmic Approaches to Control Theory and System Modeling)

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27 pages, 5382 KB

Open AccessArticle

PI-D^Æ: 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

Cited by 2 | Viewed by 2758

Abstract

This study proposes an enhanced derivative control strategy, named

P I

D^{Æ}

, 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

P I

D^{Æ}

, 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

P I

D^{Æ}

controller, which adapts in real time to changing conditions. The results show significant performance improvements. Simulation results on two systems demonstrate that

P I

D^{Æ}

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,

P I

D^{Æ}

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 KB

Open AccessArticle

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 1113

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 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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Other

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10 pages, 3376 KB

Open AccessBrief Report

Fuzzy PID Speed Control System for Sprayer Vehicles Based on Canopy Density

by Yanxin Wang, Nwabueze Emekwuru, Chengqian Jin and Fernando Auat Cheein

Algorithms 2026, 19(5), 400; https://doi.org/10.3390/a19050400 - 16 May 2026

Viewed by 556

Abstract

This study proposes an intelligent spraying vehicle speed control system integrating real-time canopy density detection with a fuzzy PID control algorithm. Utilizing LiDAR-acquired 3D point cloud data for canopy density calculation, the system dynamically adjusts PID parameters through fuzzy logic to achieve coordinated optimization of vehicle speed and spray volume. Based on the designed canopy density prediction model, a MATLAB/Simulink co-simulation framework integrating canopy perception with vehicle dynamics was established. Simulation results based on the MATLAB/Simulink platform demonstrate that the fuzzy PID controller achieves superior performance compared to conventional PID control. While maintaining a tracking accuracy of ±0.15 m/s, the proposed controller reduces speed overshoot by 5.8 percentage points. The developed control system ensures optimal speed tracking under varying canopy conditions, providing an extensible technical framework for intelligent sprayer vehicles. Full article

(This article belongs to the Special Issue Algorithmic Approaches to Control Theory and System Modeling)

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