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
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
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
- machine learning
- control theory
- dynamical systems optimization
- nonlinear dynamics
- reinforcement learning
- adaptive control
- optimal control
- physics-informed machine learning
- intelligent control
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.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
