Advanced Control Systems and Control Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 January 2025 | Viewed by 464
Special Issue Editors
Interests: control theory; advanced control theory; network; stability analysis; systems dynamics; stability; nonlinear dynamics; MATLAB simulation; modeling and simulation; Kalman filtering
Special Issues, Collections and Topics in MDPI journals
Interests: energy systems; sustainability; industrial engineering and management; soft computing
Special Issues, Collections and Topics in MDPI journals
Interests: unmanned aerial vehicles; autonomous and multi-agent systems; control systems; probabilistic risk assessment; applications to robotic and aerospace systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As our world is becoming increasingly automated and interconnected, the need for robust, efficient, and intelligent control systems has never been more critical. This Special Issue is dedicated to exploring the forefront of this vital field, highlighting groundbreaking research and innovations that address contemporary challenges. Our focus includes the following topics. Advances and applications in reinforcement learning: introducing adaptive algorithms that enhance decision-making processes, allowing systems to learn and improve over time. Theoretical physics-informed learning: bridging the gap between machine learning and traditional physics, providing robust models that respect and leverage known physical laws. Path planning in complex environments: this is pivotal for the efficient and safe navigation of autonomous vehicles and robots. Multi-agent systems: enabling coordinated actions among multiple autonomous entities, unlocking new possibilities in complex, dynamic environments. Advances in fault-tolerant control systems: these are essential for maintaining functionality in the face of component failures, ensuring reliability and safety in critical applications.
This Issue aims to inspire and inform researchers, practitioners, and enthusiasts by showcasing the latest advancements in this field and fostering a deeper understanding of these transformative technologies. Join us in exploring the exciting developments that are shaping the future of control engineering.
Dr. Francisco Rego
Dr. Ricardo Simões Santos
Dr. Sylvain Bertrand
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. Applied Sciences 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 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
- advanced control systems
- control engineering
- reinforcement learning
- physics-informed machine learning
- path planning
- autonomous vehicles
- multi-agent systems
- fault-tolerant control
- intelligent control
- robotics
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