Advanced Dynamics and Control Theory with Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 411

Special Issue Editors

School of Aeronautics, Harbin Institute of Technology, Harbin 150001, China
Interests: data-driven; fault diagnosis; deep learning algorithm
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Guest Editor
National Engineering Research Center for Medical Big Data Application Technology, Big Data Institute, Central South University, Changsha, China
Interests: artificial intelligence; integrable systems; nonlinear waves

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advancements and innovative applications in the broad field of advanced dynamics and control theory. It aims to explore cutting-edge theoretical developments, including but not limited to nonlinear dynamics, robust and adaptive control, distributed multi-agent systems, and data-driven control methodologies. A key objective is to bridge the gap between theoretical research and practical engineering applications, highlighting how novel control strategies can address complex challenges in areas such as robotics, autonomous systems, aerospace engineering, and smart infrastructure. We invite high-quality contributions that present original research, comprehensive reviews, and insightful case studies that demonstrate the transformative impact of advanced dynamics and control solutions in real-world scenarios.

Dr. Yi Ji
Dr. Tingting Jia
Guest Editors

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Keywords

  • nonlinear dynamical systems
  • control theory
  • optimization adaptive
  • robust control
  • multi-agent systems
  • game dynamics
  • data-driven modelling and analysis

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Published Papers (1 paper)

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Research

23 pages, 5954 KB  
Article
Data-Driven Prognostics for Anomalous Conditions in Aircraft Hydraulic System
by Wentao Gao, Gen Li, Wulin Zhang, Ruiqi Jiang and Yi Ji
Mathematics 2026, 14(12), 2098; https://doi.org/10.3390/math14122098 - 11 Jun 2026
Viewed by 111
Abstract
This paper systematically investigates the performance of data-driven algorithms for fault diagnosis in aircraft hydraulic systems. Firstly, the hydraulic system of an aircraft is modeled in AMESim software, and five typical faults are artificially injected. The pressure and flow curves from different position [...] Read more.
This paper systematically investigates the performance of data-driven algorithms for fault diagnosis in aircraft hydraulic systems. Firstly, the hydraulic system of an aircraft is modeled in AMESim software, and five typical faults are artificially injected. The pressure and flow curves from different position sensors are extracted to construct the fault diagnosis dataset. Then, a multi-level feature extraction method based on deep learning algorithms, including 1DFFCNN, stacked LSTM, and improved CNN-LSTM-Attention, is designed to identify the sensitive features of potential abnormal behaviors. Finally, we study the sensitivity of multi-source heterogeneous response data of the hydraulic system to the degradation of the hydraulic system’s state, and establish the correlation between the evolution of the hydraulic system’s working state and the multi-source heterogeneous response data, achieving the early prognostics of abnormal states of the hydraulic system. Numerical experiments demonstrate that the accuracy rate of the aircraft fault diagnosis based on the data-driven algorithm presented in this paper exceeds 98%. Full article
(This article belongs to the Special Issue Advanced Dynamics and Control Theory with Applications)
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