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Computational Methods and Models in Intelligent Control and Pattern Recognition
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Computational Methods and Models in Intelligent Control and Pattern Recognition

A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 1839

Editors

Prof. Dr. Shoutao Li

E-Mail Website
Guest Editor
Department of Control Theory and Control Engineering, Jilin University, Changchun 130025, China
Interests: research on intelligent machinery and robot control methods; automotive electronic control
Prof. Dr. Bo Zhao

E-Mail Website
Guest Editor
School of Systems Science, Beijing Normal University, Beijing 100875, China
Interests: adaptive dynamic programming; reinforcement learning; robot control; optimal control; continual learning; neural architecture search
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence is revolutionizing the fields of control engineering and pattern recognition, enabling unprecedented capabilities in system autonomy, adaptability, and decision-making. The rapid advancement of machine learning, deep reinforcement learning and evolutionary computation, coupled with the growing complexity of modern systems, presents both significant challenges and remarkable opportunities. This cross-disciplinary convergence is pivotal for developing next-generation intelligent systems that are capable of operating efficiently in dynamic and uncertain environments.

This Special Issue aims to share the latest research and innovative development at the intersection of intelligent control, computational intelligence, and pattern recognition. We seek to foster the exchange of novel ideas and practical solutions that address the core problems of modeling, optimization and control in complex systems. By bringing together cutting-edge theoretical research and impactful applications, this Special Issue will serve as a platform for academics and industry practitioners to explore future directions of intelligent automation.

We invite the submission of high-quality original research and comprehensive review articles that contribute to the advancement of this field. Potential topics include, but are not limited to, the following:

  • Intelligent control theory and applications for robotics and autonomous systems;
  • Advanced pattern recognition and computer vision techniques;
  • Deep learning and neural architecture search for system modeling;
  • Adaptive dynamic programming and reinforcement learning for optimal control;
  • Computational intelligence in automotive electronic control;
  • Fuzzy logic systems and intelligent decision making;
  • Evolutionary algorithms for complex system optimization;
  • Intelligent machinery and human–machine collaboration;
  • Optimization and implementation of renewable energy.

Prof. Dr. Shoutao Li
Prof. Dr. Bo Zhao
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. Mathematics 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 2600 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
  • computational intelligence
  • pattern recognition
  • reinforcement learning
  • robotic control
  • adaptive dynamic programming
  • machine learning
  • autonomous systems

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Published Papers (3 papers)

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Research

24 pages, 3810 KB  
Article
A Birth–Death Queueing Model for Optimal Threshold Recruitment in Manpower Organizations
by Ramasamy Sivasamy, Keamogetse Setlhare, Wilford Molefe and Boikanyo Mokgweetsi
Mathematics 2026, 14(15), 2676; https://doi.org/10.3390/math14152676 - 24 Jul 2026
Viewed by 293
Abstract
This study develops two queuing manpower models, QMPM-1 and QMPM-2, where the manpower sequence evolves on a finite state space as a birth–death process governed by a threshold-recruitment level ‘L’. Random attrition follows a Poisson process with a positive mean rate. However, beyond [...] Read more.
This study develops two queuing manpower models, QMPM-1 and QMPM-2, where the manpower sequence evolves on a finite state space as a birth–death process governed by a threshold-recruitment level ‘L’. Random attrition follows a Poisson process with a positive mean rate. However, beyond the level L, the attrition rate rises depending on the sensitivity to workforce overload that comes from congestion. Inter-recruitment times follow an exponential law with an average positive rate. In QMPM–1, recruitment is stopped when the workforce exceeds L, while in QMPM–2, a reduced but strictly positive recruitment rate is maintained even in the overloaded region. The study obtains both transient and stationary solutions and verifies the stability of QMPM-1 using a Foster–Lyapunov drift criterion. A long-run cost function involving staffing, recruitment, and congestion penalties is formulated to determine the optimal threshold. Numerical illustrations support that weak recruitment compared to attrition ratio results in low optimal thresholds, while the stronger recruitment effectiveness allows for higher thresholds that can favor larger and more stable workforce levels. Full article
(This article belongs to the Special Issue Computational Methods and Models in Intelligent Control and Pattern Recognition)
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50 pages, 1647 KB  
Article
State-Space Construction of Continuous-Time Orthogonal Systems with Applications to System Identification and Control
by Josip Kasać, Vladimir Milić, Denis Kotarski and Danijel Pavković
Mathematics 2026, 14(14), 2662; https://doi.org/10.3390/math14142662 - 22 Jul 2026
Viewed by 508
Abstract
Continuous-time orthogonal basis functions play a fundamental role in system identification, signal approximation, model reduction, and control, where compact and numerically efficient representations of dynamical systems are required. Most existing constructions are based on predefined frequency-domain basis functions and their associated pole configurations. [...] Read more.
Continuous-time orthogonal basis functions play a fundamental role in system identification, signal approximation, model reduction, and control, where compact and numerically efficient representations of dynamical systems are required. Most existing constructions are based on predefined frequency-domain basis functions and their associated pole configurations. This paper introduces a novel state-space framework for the construction of continuous-time orthogonal and biorthogonal systems by characterizing orthogonality directly through the dynamics of stable linear systems. The central result shows that orthogonality can be enforced by a Lyapunov-type matrix condition linking the system matrix and the initial state, thereby enabling the systematic generation of orthogonal basis functions as state trajectories. The proposed framework naturally encompasses the classical Laguerre and Kautz systems as special cases while providing substantially greater design flexibility through the independent parametrization of system matrices and initial conditions. It is further extended to output-orthogonal, state-biorthogonal, and output-biorthogonal systems, yielding a unified state-space formulation of orthogonal and dual functional representations. In addition, new algebraic formulations of convolution and signal decomposition are derived, leading to explicit coefficient relations expressed through Lyapunov and Sylvester matrix equations. The applicability of the proposed framework is demonstrated through numerical examples in system identification, model reduction, and optimal control. The results show that the additional parametrization freedom enables orthogonal representations that are better adapted to oscillatory and weakly damped dynamics while preserving the computational advantages of orthogonal-function-based approaches. Full article
(This article belongs to the Special Issue Computational Methods and Models in Intelligent Control and Pattern Recognition)
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26 pages, 5703 KB  
Article
An Evolutionary Neural-Enhanced Intelligent Controller for Robotic Visual Servoing Under Non-Gaussian Noise
by Xiaolin Ren, Haobing Cui, Haoyu Yan and Yidi Liu
Mathematics 2026, 14(4), 653; https://doi.org/10.3390/math14040653 - 12 Feb 2026
Viewed by 620
Abstract
Accurate state estimation is essential for the performance of uncalibrated visual servoing systems, yet it is frequently undermined by non-Gaussian disturbances—such as impulse noise, motion blur, and occlusions—whose heavy-tailed statistical characteristics are not adequately represented by conventional Gaussian models. To address this issue, [...] Read more.
Accurate state estimation is essential for the performance of uncalibrated visual servoing systems, yet it is frequently undermined by non-Gaussian disturbances—such as impulse noise, motion blur, and occlusions—whose heavy-tailed statistical characteristics are not adequately represented by conventional Gaussian models. To address this issue, this paper presents an evolutionary neural-enhanced intelligent controller designed for robotic visual servoing under such noise conditions. The controller architecture incorporates a hybrid estimation core that integrates α-stable distribution modeling for principled noise characterization with an Interacting Multiple Model Kalman filter (IMM-KF) to address system dynamics and uncertainties. A multi-layer perceptron (MLP), optimized globally via the Stochastic Fractal Search (SFS) algorithm, is embedded to provide adaptive compensation for residual estimation errors. This integration of statistical modeling, adaptive filtering, and evolutionary optimization constitutes a coherent learning-based control framework. Simulations and physical experiments reveal that the proposed method enhances improvements in estimation accuracy and tracking performance relative to conventional approaches. The outcomes indicate that the framework offers a functional solution for vision-based robotic systems operating under realistic conditions where non-Gaussian sensor noise is present. Full article
(This article belongs to the Special Issue Computational Methods and Models in Intelligent Control and Pattern Recognition)
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