Nonlinear Dynamical Systems: Stability, Control, and Data-Driven Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 31 May 2027 | Viewed by 64
Editors
Interests: intelligent control theory; deep learning algorithms; unmanned aerial vehicles (UAVs); robotics; autonomous navigation; multi-agent collaborative control; real-time motion planning
Interests: adaptive control; event-triggered control; anti-disturbance control
Interests: deep graph learning; data engineering; cybersecurity; edge intelligence; applied AI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Nonlinear dynamical systems arise widely in natural, engineering, and social sciences, including robotics, energy systems, transportation networks, biological processes, climate dynamics, and financial systems. Their complex behaviours, such as bifurcation, chaos, synchronization, and uncertainty, pose significant challenges for mathematical analysis, prediction, and control. Recent advances in artificial intelligence, machine learning, and data-driven methodologies provide powerful new tools for understanding and managing such systems. This Special Issue aims to bring together high-quality research on the mathematical foundations, analytical methods, and computational approaches for AI-driven nonlinear dynamical systems. Particular attention will be given to stability analysis, control design, learning-based modelling, data-driven prediction, reinforcement learning, neural-network-based control, physics-informed learning, Koopman operator methods, adaptive and robust control, complex networks, and multi-agent systems. The Special Issue welcomes both theoretical and applied contributions that develop rigorous mathematical frameworks, innovative algorithms, or practical solutions for nonlinear dynamical systems. By highlighting the interaction between artificial intelligence and nonlinear dynamics, this collection seeks to promote new insights, methods, and applications, and to encourage interdisciplinary collaboration among mathematicians, control theorists, engineers, computer scientists, and researchers working on complex systems.
Prof. Dr. Zhanshan Zhao
Prof. Dr. Haibin Sun
Dr. Jiao Yin
Guest Editors
Manuscript Submission Information
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Keywords
- nonlinear systems
- nonlinear control
- learning-based modelling
- data-driven prediction
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