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Advance in Modeling, Cooperative Control, and Decision-Making Method for the...
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Advance in Modeling, Cooperative Control, and Decision-Making Method for the Collective Large-Scale Intelligent Systems

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2229

Special Issue Editors

Prof. Dr. Zhi Li

E-Mail Website
Guest Editor
School of Mechano-Electronic Engineering, Xidian University, Xi'an 710000, China
Interests: complex multi-intelligent network; collaborative control theory and application; group intelligent decision-making and optimization; collaborative control of multiple drones, unmanned vehicles, and robots
Special Issues, Collections and Topics in MDPI journals
Dr. Sihai Guan

E-Mail Website
Guest Editor
College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
Interests: adaptive signal processing; blood oxygen level dependent (BOLD) signal analysis; machine learning; entropy and fractal analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled “Advance in Modeling, Cooperative Control, and Decision-Making Method for the collective large-scale intelligent systems”, is dedicated to researchers specializing in the modeling, cooperative control, and decision-making methods within collective large-scale intelligent systems. The primary objective of this Special Issue is to collect the latest advancements in modeling methods (e.g., data-driven and model-driven, etc.), cooperative control strategies (e.g., distributed control and adaptive control, etc.), and decision-making methods (e.g., deep learning and game theory, etc.) and provide significant theoretical support and practical methodologies for collective large-scale intelligent systems, helping to solve practical problems. This Special Issue is dedicated to a wide range of scientific subjects, including collective large-scale intelligent systems, artificial intelligence, decision-making methods, and cooperative control theory.

The scope of this Special Issue covers a broad array of topics, including, but not limited to, the following:

  • The modeling, control, and decision making of collective large-scale intelligent systems;
  • Multi-agent cooperative control and optimization;
  • Adaptive estimation;
  • The application of artificial intelligence technology;
  • Game evolution and intelligent decision making;
  • Information fusion;
  • Intelligent monitoring and tracking;
  • A stability and complexity analysis of dynamical systems;

We look forward to receiving your contributions and expertise.

Prof. Dr. Zhi Li
Dr. Sihai Guan
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. 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

  • the modeling, control, and decision making of collective large-scale intelligent systems
  • multi-agent cooperative control and optimization
  • adaptive estimation
  • the application of artificial intelligence technology
  • game evolution and intelligent decision making
  • information fusion
  • intelligent monitoring and tracking
  • a stability and complexity analysis of dynamical systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (5 papers)

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Research

23 pages, 2147 KiB  
Article
Precision Fixed-Time Formation Control for Multi-AUV Systems with Full State Constraints
by Yuanfeng Chen, Haoyuan Wang and Xiaodong Wang
Mathematics 2025, 13(9), 1451; https://doi.org/10.3390/math13091451 - 28 Apr 2025
Viewed by 24
Abstract
The trajectory tracking the control of autonomous underwater vehicle (AUV) systems faces considerable challenges due to strong inter-axis coupling and complex time-varying external disturbances. This paper proposes a novel fixed-time control scheme incorporating a switching threshold-based event-driven strategy to address critical issues in [...] Read more.
The trajectory tracking the control of autonomous underwater vehicle (AUV) systems faces considerable challenges due to strong inter-axis coupling and complex time-varying external disturbances. This paper proposes a novel fixed-time control scheme incorporating a switching threshold-based event-driven strategy to address critical issues in multi-AUV formation control, including full-state constraints, unmeasurable states, model uncertainties, limited communication resources, and unknown time-varying disturbances. A rapid and stable dimensional augmented state observer (RSDASO) was first developed to achieve fixed-time convergence in estimating aggregated disturbances and unmeasurable states. Subsequently, a logarithmic barrier Lyapunov function was constructed to derive a fixed-time control law that guarantees bounded system errors within a predefined interval while strictly confining all states to specified constraints. The introduction of a switching threshold event-triggering mechanism (ETM) significantly reduced communication resource consumption. The simulation results demonstrate the effectiveness of the proposed method in improving control accuracy while substantially lowering communication overhead. Full article
(This article belongs to the Special Issue Advance in Modeling, Cooperative Control, and Decision-Making Method for the Collective Large-Scale Intelligent Systems)
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17 pages, 3681 KiB  
Article
Control of Vehicle Lateral Handling Stability Considering Time-Varying Full-State Constraints
by Lizhe Wu and Dingxuan Zhao
Mathematics 2025, 13(8), 1217; https://doi.org/10.3390/math13081217 - 8 Apr 2025
Viewed by 246
Abstract
Lateral handling stability control is crucial for ensuring vehicle driving safety. To address this issue, this paper proposes a lateral handling stability control method that considers time-varying full-state constraints. By constructing a time-varying symmetric Barrier Lyapunov Function (TS-BLF), this method imposes time-varying nonlinear [...] Read more.
Lateral handling stability control is crucial for ensuring vehicle driving safety. To address this issue, this paper proposes a lateral handling stability control method that considers time-varying full-state constraints. By constructing a time-varying symmetric Barrier Lyapunov Function (TS-BLF), this method imposes time-varying nonlinear constraints on both the sideslip angle and yaw rate, thereby ensuring full-state constrained stability control of vehicles under complex operating conditions. Additionally, a second-order command filtering technique with an error compensation mechanism is introduced to reduce the computational complexity of control laws while mitigating filter-induced errors that may degrade system performance. To validate the effectiveness and robustness of the proposed method, the vehicle’s dynamic response is analyzed under different speeds on both dry asphalt pavement and dry gravel surfaces. The simulation results demonstrate that the proposed method effectively suppresses understeer and oversteer, enhances the dynamic stability margin under extreme operating conditions, and improves vehicle adaptability in complex environments. Full article
(This article belongs to the Special Issue Advance in Modeling, Cooperative Control, and Decision-Making Method for the Collective Large-Scale Intelligent Systems)
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29 pages, 3532 KiB  
Article
Dynamic Modeling and Disturbance-Observer-Enhanced Control for Mecanum-Wheeled Vehicles Under Load and Noise Disturbance
by Chensheng Li and Zhi Li
Mathematics 2025, 13(5), 789; https://doi.org/10.3390/math13050789 - 27 Feb 2025
Viewed by 504
Abstract
This paper investigates the dynamic modeling and robust control of a Mecanum-wheeled vehicle (MWV) under load disturbances and measurement noise. The system is modeled as a cascaded state-space representation, where the motor transfer function (PWM input → torque output) and the vehicle transfer [...] Read more.
This paper investigates the dynamic modeling and robust control of a Mecanum-wheeled vehicle (MWV) under load disturbances and measurement noise. The system is modeled as a cascaded state-space representation, where the motor transfer function (PWM input → torque output) and the vehicle transfer function (torque input → vehicle speed output) are combined. The PWM-induced motor delay is linearized, and the complete dynamic model is derived using Lagrangian mechanics, addressing the limitations of conventional models that are incomplete and unable to decouple control signals from disturbance signals. For the developed model, a robust stability controller is designed by integrating Internal Model Control (IMC) with a Disturbance Observer (DOB), enhancing real-time disturbance rejection. Open-loop experiments validate the model’s accuracy, showing a Dynamic Time Warping (DTW) error of 0.2662 m, significantly lower than the 0.3198 m observed in traditional models. In closed-loop simulations, under load disturbances (TL=0.1 to TL=−0.7) and Gaussian noise (power: 0.0001–0.00005), the proposed IMC + DOB controller achieves 97.6% faster stabilization than IMC and 98.3% faster than PID, demonstrating superior convergence speed, robustness, and disturbance rejection. This study provides a novel control strategy that effectively handles non-square system dynamics while mitigating external disturbances in real time. The proposed framework enhances trajectory tracking accuracy and stability, with potential applications in autonomous robotics and vehicular systems. Full article
(This article belongs to the Special Issue Advance in Modeling, Cooperative Control, and Decision-Making Method for the Collective Large-Scale Intelligent Systems)
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22 pages, 5596 KiB  
Article
URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection
by Hailong Xi, Le Ru, Jiwei Tian, Bo Lu, Shiguang Hu, Wenfei Wang and Xiaohui Luan
Mathematics 2025, 13(4), 591; https://doi.org/10.3390/math13040591 - 11 Feb 2025
Viewed by 646
Abstract
In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with adversarial patches being a relatively straightforward method to implement. [...] Read more.
In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with adversarial patches being a relatively straightforward method to implement. However, current research on adversarial patches, especially those targeting UAV object detection, is limited. This scarcity is notable given the complex and dynamically changing environment inherent in UAV image acquisition, which necessitates the development of more robust adversarial patches to achieve effective attacks. To address the challenge of adversarial attacks in UAV high-altitude reconnaissance, this paper presents a robust adversarial patch generation framework. Firstly, the dataset is reconstructed by considering various environmental factors that UAVs may encounter during image collection, and the influences of reflections and shadows during photography are integrated into patch training. Additionally, a nested optimization method is employed to enhance the continuity of attacks across different altitudes. Experimental results demonstrate that the adversarial patches generated by the proposed method exhibit greater robustness in complex environments and have better transferability among similar models. Full article
(This article belongs to the Special Issue Advance in Modeling, Cooperative Control, and Decision-Making Method for the Collective Large-Scale Intelligent Systems)
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14 pages, 740 KiB  
Article
Containment Control for High-Order Heterogeneous Continuous-Time Multi-Agent Systems with Input Nonconvex Constraints
by Jiahao Xu, Yaozhong Wang and Wenguang Zhang
Mathematics 2025, 13(3), 509; https://doi.org/10.3390/math13030509 - 3 Feb 2025
Viewed by 636
Abstract
This article investigates containment control for high-order heterogeneous continuous-time multi-agent systems (MASs) with input nonconvex constraints, bounded communication delays and switching topologies. Firstly, we introduce a scaling factor for the constraint operator to obtain an equivalent unconstrained system model. Following equivalent model transformations, [...] Read more.
This article investigates containment control for high-order heterogeneous continuous-time multi-agent systems (MASs) with input nonconvex constraints, bounded communication delays and switching topologies. Firstly, we introduce a scaling factor for the constraint operator to obtain an equivalent unconstrained system model. Following equivalent model transformations, we analyze the maximum distance from all agents to the convex hull spanned by leaders using norm-based differentiation. It is demonstrated that, within high-order heterogeneous continuous-time MASs subject to control input nonconvex constraints, the convergence of each agent into the convex hull spanned by leaders is guaranteed, provided that there exists at least one directed path from any leader to each agent within the union of communication topologies. Simulation examples are presented to validate the theoretical findings. Full article
(This article belongs to the Special Issue Advance in Modeling, Cooperative Control, and Decision-Making Method for the Collective Large-Scale Intelligent Systems)
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