Data-Driven Control for Vehicle Dynamics
A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Control Systems".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 97
Special Issue Editor
Interests: vehicle dynamics; machine learning; wind turbine; reinforcement learning; sliding mode control; fault-tolerant control
Special Issue Information
Dear Colleagues,
Actuators are crucial components in managing energy transformation in electric, hybrid, and fuel-cell vehicles, demanding top-tier control strategies to maintain maximum efficiency. Forecasts suggest that, by 2040, autonomous vehicles are expected to represent about 50% of vehicle purchases, 30% of the total vehicle population, and 40% of total vehicle mileage, amplifying the need for sophisticated actuation and control systems. This Special Issue focuses on electrification and cutting-edge developments in fault-tolerant control systems for vehicle dynamics, emphasizing path tracking of autonomous vehicles as well as sophisticated control in various vehicular aspects like gear shift systems, electronic locker systems, suspension systems, and electric limited slip differentials. The aim is to explore the integration of machine learning (ML)-based approaches to enhance control strategies and improve system reliability across various operating conditions. Contributors are expected to discuss the application of data-driven approaches to derive robust control systems that increase the reliability of vehicles, supporting automotive manufacturers in achieving cost and time efficiencies. Papers that provide scalable control solutions adaptable to new technologies and vehicle models are particularly welcome. This issue seeks to foster discussions on innovative solutions, inviting researchers to contribute their insights on data-driven approaches to actuator control in the automotive sector, particularly as it pertains to the challenges and opportunities in developing complex vehicle subsystems for better operating characteristics.
This Special Issue addresses the progression and implementation of advanced data-driven technologies developed for modern vehicular systems, with a spotlight on a variety of control applications in cutting-edge vehicle frameworks. Topics to be explored include the following:
- Theoretical and practical approaches to modeling, predicting, and managing the driving patterns of autonomous vehicles;
- Innovations in vehicle dynamics and control mechanisms;
- The application of predictive and machine learning techniques to enhance the safety and efficiency of autonomous driving systems;
- Advanced estimation and sensory technologies for autonomous vehicle navigation and decision-making;
- Strategies to mitigate vibrations and other negative effects from electromechanical interactions in vehicles with in-wheel motors and active suspension systems.
Dr. Yashar Mousavi
Guest Editor
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
- autonomous vehicles
- fault-tolerant control
- machine learning in vehicle dynamics
- electromechanical coupling
- predictive control technologies
- vibration suppression techniques
- vehicle-to-user interaction
- energy efficient actuators
- actuator control
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.
