Control Theory and Computational Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 852

Special Issue Editors

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Guest Editor
Centro Nacional de Investigación y Desarrollo Tecnológico, Mexico, Cuernavaca, Mexico
Interests: control theory; computational intelligence

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Guest Editor
Electronic Engineering Department, TecNM/CENIDET, Cuernavaca 62490, Mexico
Interests: automatic control; mathematical modeling; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the fields of control theory and computational intelligence have undergone significant theoretical and application advancements. Furthermore, the combined applications of these two disciplines have revolutionized current technological developments and have established the theoretical foundations for future research. Therefore, this Special Issue aims to collate theoretical and application research on the different subdisciplines of control theory and computational intelligence, such as nonlinear and linear control theory, fault-tolerant control systems, nonlinear and linear estimation systems, neural networks, fuzzy systems, and evolutionary computation.

We cordially invite researchers working in automatic control and computational intelligence fields to submit their novel studies.

Prof. Dr. Ricardo Fabricio Escobar-Jiménez
Prof. Dr. Manuel Adam Medina
Guest Editors

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  • control systems
  • stability theory
  • nonlinear and linear estimation
  • fault-tolerant systems
  • diagnosis systems
  • artificial neural networks
  • fuzzy systems
  • evolutionary computation
  • applied mathematics
  • ordinary differential equations
  • fractional differential equations

Published Papers (1 paper)

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26 pages, 2893 KiB  
Fractional-Order Sliding Mode Observer for Actuator Fault Estimation in a Quadrotor UAV
by Vicente Borja-Jaimes, Antonio Coronel-Escamilla, Ricardo Fabricio Escobar-Jiménez, Manuel Adam-Medina, Gerardo Vicente Guerrero-Ramírez, Eduardo Mael Sánchez-Coronado and Jarniel García-Morales
Mathematics 2024, 12(8), 1247; - 20 Apr 2024
Viewed by 584
In this paper, we present the design of a fractional-order sliding mode observer (FO-SMO) for actuator fault estimation in a quadrotor unmanned aerial vehicle (QUAV) system. Actuator faults can significantly compromise the stability and performance of QUAV systems; therefore, early detection and compensation [...] Read more.
In this paper, we present the design of a fractional-order sliding mode observer (FO-SMO) for actuator fault estimation in a quadrotor unmanned aerial vehicle (QUAV) system. Actuator faults can significantly compromise the stability and performance of QUAV systems; therefore, early detection and compensation are crucial. Sliding mode observers (SMOs) have recently demonstrated their accuracy in estimating faults in QUAV systems under matched uncertainties. However, existing SMOs encounter difficulties associated with chattering and sensitivity to initial conditions and noise. These challenges significantly impact the precision of fault estimation and may even render fault estimation impossible depending on the magnitude of the fault. To address these challenges, we propose a new fractional-order SMO structure based on the Caputo derivative definition. To demonstrate the effectiveness of the proposed FO-SMO in overcoming the limitations associated with classical SMOs, we assess the robustness of the FO-SMO under three distinct scenarios. First, we examined its performance in estimating actuator faults under varying initial conditions. Second, we evaluated its ability to handle significant chattering phenomena during fault estimation. Finally, we analyzed its performance in fault estimation under noisy conditions. For comparison purposes, we assess the performance of both observers using the Normalized Root-Mean-Square Error (NRMSE) criterion. The results demonstrate that our approach enables more accurate actuator fault estimation, particularly in scenarios involving chattering phenomena and noise. In contrast, the performance of classical (non-fractional) SMO suffers significantly under these conditions. We concluded that our FO-SMO is more robust to initial conditions, chattering phenomena, and noise than the classical SMO. Full article
(This article belongs to the Special Issue Control Theory and Computational Intelligence)
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