Data-Driven Control and Optimization for Autonomous Systems
A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Control Systems".
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 616
Special Issue Editors
Interests: data-driven control; reinforcement learning; distributed optimization
Interests: distributed optimization and learning; reinforcement learning; networked control systems
Special Issue Information
Dear Colleagues,
Although bolstered by recent success in dealing with high-dimensional data in science and engineering applications, data-centric control and optimization algorithms do not scale well to large systems in addition to being susceptible to adversarial noise or attacks. Various data-driven control and optimization (DADCO) algorithms have been proposed, and they are indeed being used in many safety- and security-critical autonomous systems. Yet, most existing DADCO methods do not scale well nor exhibit a desired level of robustness to large-scale networked physical systems. Advances in robust and communication-efficient optimization, event-triggered and data-driven control, hybrid data- and model-driven control, bilevel and minimax optimization, and deep reinforcement learning all provide a timely opportunity to re-innovate existing DADCO algorithms. To this end, the aim of this Special Issue (SI) is to develop principled DADCO methods for cyber-physical multi-agent systems and performance that guarantee incorporating stability and robustness. We envision the proposed SI not only as a way to better introduce automatic control researchers to the field of DADCO but also as a timely opportunity to broaden the impact of the field.
Topics of contributing papers can include but are not limited to:
- Hybrid model-/data-driven control, optimization, and decision-making for autonomous systems
- Deep, reinforcement, and meta learning for autonomous systems
- Safe, secure, and scalable reinforcement learning and adaptive control for autonomous systems
- Resilient control and security analysis for autonomous systems under attacks
- Data-driven fault diagnosis and recovery for autonomous systems
- Formal analysis and verification of safety-critical autonomous systems
- Physics-constrained learning for shared autonomy in autonomous systems
Prof. Dr. Gang Wang
Dr. Keyou You
Dr. Xianlin Zeng
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.
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.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
