Machine Learning for Actuation and Control in Robotic Joint Systems
A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Robotics".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 12
Special Issue Editors
Interests: robot joint design; motion control; machine learning
Interests: robot control; multi-agent cooperative control; high-precision control of electromechanical systems; active disturbance rejection control; advanced robust control; control theory and application
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Robotic joint systems are fundamental to modern robotics, enabling precise motion, adaptability, and autonomy in applications ranging from industrial automation to biomedical devices. Recent advances in machine learning (ML) have opened new possibilities for enhancing the actuation and control of these systems, improving efficiency, robustness, and adaptability in dynamic environments. This Special Issue seeks to explore cutting-edge research at the intersection of ML, actuation technologies, and control strategies for robotic joints.
We aim to showcase innovative methodologies, experimental validations, and reviews that address challenges and opportunities in ML-driven robotic joint systems. Researchers are encouraged to submit original work that bridges the gap between theoretical ML advancements and practical robotic applications.
Dr. Gao Huang
Dr. Pan Yu
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. Actuators is an international peer-reviewed open access monthly 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 2400 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
- robotic joint control
- robot joint design
- machine learning
- modeling, control, and optimization of electromechanical systems
- adaptive control
- neural networks
- soft robot and actuation
- humanoid robot
- bio-inspired robot design
- optimization of robot transmission
- machine learning-based motion control
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