Data-Driven and Learning-Based Control for Vehicle Applications
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 1162
Special Issue Editors
Interests: control design and automation; connected and automated vehicles; electric and hybrid vehicles; optimization; artificial intelligence
Special Issue Information
Dear Colleagues,
This Special Issue seeks new and creative applications of emerging data-driven and learning-based control techniques to vehicle systems. The pace of innovation in the auto industry is accelerating quickly to include and merge connectivity, automation, and electrification in ways that will significantly transform the next generation of vehicles. The complexity of modern vehicles requires the use of advanced control methods applicable to challenging problems involving systems with unknown and changing dynamics, or interactions with highly uncertain environments, where specific safety or performance constraints must be satisfied at the same time. Data-driven and learning-based control techniques leverage online or offline data obtained from a complex system, using learning techniques to identify a proper data-driven system model for control design, or to derive appropriate control laws directly.
For this Special Issue, we are looking for original contributions or comprehensive tutorial and survey articles involving elegant machine learning techniques, advanced variants of reinforcement learning approaches, or other state-of-the-art data-driven methods to create suitable system models for control design, or to generate high-performance control laws directly for challenging automotive problems, including, but not limited to: automation and advanced driver assistance systems, energy management systems, battery management systems, thermal comfort control, and other complex vehicle control systems.
Dr. Nasser Lashgarian Azad
Dr. Yuan Lin
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. Machines 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
- automation and advanced driver assistance systems
- energy management systems
- battery management systems
- thermal comfort control
- complex vehicle control systems
