Advanced Learning and Intelligent Control Algorithms for Robots
A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Control Systems".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 28
Special Issue Editors
Interests: nonlinear control; robotics
Special Issues, Collections and Topics in MDPI journals
Interests: modelling and cooperative control for complex network systems
Interests: systems control and machine learning; specifically including data-driven modeling (building predictive models using system identification and deep learning approaches); model reduction (simplifying models of complex dynamic systems for prediction and control); optimization for control (designing control strategies using optimal control and reinforcement learning)
Special Issue Information
Dear Colleagues,
Robots are now widely used in various sectors and applications. They have become an integral part of modern automation, performing essential functions in industrial automation, logistics, healthcare, transportation, search and rescue, environmental monitoring, and exploration. This Special Issue focuses on exploring the profound theories and diverse applications of advanced learning and intelligent control algorithms for robots. It aims to bring together scholars and professionals from academia and industry to share their recent discoveries, techniques, and practical uses that drive the development of this field.
This Special Issue welcomes original submissions covering various aspects of advanced learning and intelligent control algorithms for robots. We are interested in original works that explore the theoretical foundations, practical implementations, and real-world deployments in, but not limited to, the following areas:
- Advanced learning techniques for robots;
- Intelligent control algorithms for robots;
- Multi-robot cooperation and control systems;
- Autonomous navigation and control;
- Decision-making in robotic control systems;
- Learning and adaptation in robotic control;
- Swarm robotics control (coordinating collective behaviors, utilizing swarm intelligence in control, and applying swarm robotics with a focus on control);
- Applications of advanced learning and intelligent control algorithms in robots;
- Robotic-based motion control simulators;
- Emerging technologies in robotic control.
We look forward to receiving your contributions and creating an outstanding collection of papers that will shape the future of advanced learning and intelligent control algorithms for robots.
Prof. Dr. Haibo Du
Dr. Lanlin Yu
Dr. Xiaodong Cheng
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
- robotics
- advanced learning
- intelligent control algorithms
- multi-robot control
- autonomous navigation control
- decision-making
- swarm robotics control
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