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Mathematics
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New Advances in Control Theory and Its Applications
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New Advances in Control Theory and Its Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3969

Special Issue Editor

Dr. Cong Wang

E-Mail Website
Guest Editor
School of Intelligent Science and Technology, Xinjiang University, Urumqi 830017, China
Interests: new energy power system; integrated energy system; intelligent control; intelligent optimization

Special Issue Information

Dear Colleagues,

As the core of systems science and engineering practice, control theory has evolved from classical cybernetics to modern state-space methods, continuously driving industrial innovation and technological progress. Since the foundation of classical control theory in the 20th century, its research scope has expanded from single-input, single-output systems to multi-variable, nonlinear, and networked complex systems. Moreover, it has given rise to important branches such as robust control, adaptive control, and model-predictive control.

In recent years, with the deep integration of artificial intelligence, big data, and the Internet of Things, interdisciplinary research between control theory and emerging technologies has become a hot topic. For example, data-driven control, distributed cooperative control, and intelligent control methods based on deep learning have provided new paradigms for solving difficult problems such as high-dimensional uncertain systems and the collaborative cooperation of intelligent agent swarms. Therefore, we are publishing this Special Issue in order to collect the latest research advances regarding control within nonlinear systems and popular fields.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but not limited to, the following:

  1. Chaos-control algorithms for mechanical, electrical, electronic, chemical, and other systems.
  2. Applications of fractional-order/stochastic differential equations in nonlinear control.
  3. Design of control algorithms for high-dimensional systems/neural networks.
  4. Cooperative control of multi-agent systems and optimization of swarm intelligence.
  5. Challenges and applications of intelligent control in the field of new energy
  6. Optimization and planning of control algorithms in complex systems.
  7. Security control of cyber–physical systems under cyber attacks.

We look forward to receiving your contributions.

Dr. Cong Wang
Guest Editor

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-blind 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
  • control algorithm
  • dynamic system
  • optimal 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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25 pages, 656 KB  
Article
Bayesian Optimization for the Synthesis of Generalized State-Feedback Controllers in Underactuated Systems
by Miguel A. Solis, Sinnu S. Thomas, Christian A. Choque-Surco, Edgar A. Taya-Acosta and Francisca Coiro
Mathematics 2025, 13(19), 3139; https://doi.org/10.3390/math13193139 - 1 Oct 2025
Viewed by 502
Abstract
Underactuated systems, such as rotary and double inverted pendulums, challenge traditional control due to nonlinear dynamics and limited actuation. Classical methods like state-feedback and Linear Quadratic Regulators (LQRs) are commonly used but often require high gains, leading to excessive control effort, poor energy [...] Read more.
Underactuated systems, such as rotary and double inverted pendulums, challenge traditional control due to nonlinear dynamics and limited actuation. Classical methods like state-feedback and Linear Quadratic Regulators (LQRs) are commonly used but often require high gains, leading to excessive control effort, poor energy efficiency, and reduced robustness. This article proposes a generalized state-feedback controller with its own internal dynamics, offering greater design flexibility. To automate tuning and avoid manual calibration, we apply Bayesian Optimization (BO), a data-efficient strategy for optimizing closed-loop performance. The proposed method is evaluated on two benchmark underactuated systems, including one in simulation and one in a physical setup. Compared with standard LQR designs, the BO-tuned state-feedback controller achieves a reduction of approximately 20% in control signal amplitude while maintaining comparable settling times. These results highlight the advantages of combining model-based control with automatic hyperparameter optimization, achieving efficient regulation of underactuated systems without increasing design complexity. Full article
(This article belongs to the Special Issue New Advances in Control Theory and Its Applications)
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23 pages, 10351 KB  
Article
Precision Tracking of Industrial Manipulators via Adaptive Nonsingular Fixed-Time Sliding Mode Control
by Anh Tuan Vo, Thanh Nguyen Truong, Ic-Pyo Hong and Hee-Jun Kang
Mathematics 2025, 13(16), 2641; https://doi.org/10.3390/math13162641 - 17 Aug 2025
Viewed by 664
Abstract
This paper presents a novel adaptive fixed-time sliding mode control (AFxTSMC) framework for industrial manipulators. The proposed adaptive reaching law (ARL) enables rapid and stable gain reduction by leveraging the current parameter values to maintain positivity and prevent sign reversals, thereby reducing chattering. [...] Read more.
This paper presents a novel adaptive fixed-time sliding mode control (AFxTSMC) framework for industrial manipulators. The proposed adaptive reaching law (ARL) enables rapid and stable gain reduction by leveraging the current parameter values to maintain positivity and prevent sign reversals, thereby reducing chattering. Additionally, the ARL guarantees fixed-time convergence. A singularity-free fixed-time sliding function (SF-FxTSF) ensures fast, robust, and singularity-free convergence. To enhance robustness, a modified third-order sliding mode observer (TOSMO) is integrated into the control framework. This observer estimates both internal uncertainties and external disturbances with improved estimation speed, enabling effective compensation while maintaining convergence performance. A Lyapunov-based analysis rigorously confirms the stability of the proposed method. Simulations of the SAMSUNG FARA AT2 manipulator indicate superior tracking accuracy, faster convergence, and smoother control performance compared to the three state-of-the-art methods. These results underscore the proposed method’s advantages as a robust, scalable, and high-performance control solution for industrial robotic systems. Full article
(This article belongs to the Special Issue New Advances in Control Theory and Its Applications)
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