Machine Learning for Dynamics and Control Advancement in Engineering 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 May 2026 | Viewed by 553
Special Issue Editor
Interests: nonlinear system dynamics and control; vehicle system dynamics; model-based reinforcement learning control; physically informed learning control
Special Issue Information
Dear Colleagues,
The integration of machine learning (ML) with system dynamics and control has revolutionized engineering applications, enabling data-driven modeling, adaptive control, and real-time optimization in complex, nonlinear environments. This Special Issue seeks to highlight cutting-edge research at the intersection of ML, dynamics, and control, with a focus on theoretical advances, algorithmic innovations, and practical implementations in engineering domains.
Topics of interest include, but are not limited to, the following:
- ML-based modeling of dynamical systems (e.g., neural ODEs, Koopman operators, Gaussian processes).
- Reinforcement learning and adaptive control for robotics, aerospace, or autonomous systems.
- Physics-informed ML for hybrid modeling of the nonlinear engineering systems.
- Data-driven stability analysis and robust control under uncertainty.
- Deep reinforcement learning for predictive control.
- Transfer learning and meta-learning for dynamics adaptation.
- Explainable AI in dynamics and control systems for safety-critical applications.
- Edge AI and real-time ML for embedded control systems.
Prof. Dr. Ye Zhuang
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- control theory
- dynamical systems optimization
- nonlinear dynamics
- reinforcement learning
- adaptive control
- optimal control
- physics-informed machine learning
- intelligent control
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