CALL FOR PAPERS
SPECIAL ISSUE ON "Emerging Robust and Data-Driven Control Methods for Uncertain Learning Systems"
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
Learning systems represent a particularly important class of practical data-driven systems that adapt to their environment based on the environment’s response to the system’s action. Although in some settings traditional adaptive and robust control techniques have been verified to be effective when plants that are large-scale and/or have very complex dynamics (e.g., industrial processes, power grid networks, and transportation systems), often in these plants the problems of parameter mismatch and un-modeled dynamics are encountered, and thus the techniques of robust (or adaptive) control methods in the framework of modern control are no longer sufficient. As an alternative, various learning paradigms have been established, e.g., reinforcement learning, deep learning, artificial neural networks, and iterative learning, among others. These learning paradigms construct controllers or control signals directly with the data collected and stored when the system operates, without the need of identifying system models. However, even such “model-free” control systems must make assumptions about the plant dynamics. One such common assumption is that the plant dynamics are linear. Another is that they are time-invariant (autonomous). When the dynamics do not meet these assumptions, traditional learning paradigms can fail. Despite the success of learning-based methods, control frameworks for learning systems under uncertainty in the assumptions with respect to the system dynamics are still lacking.
This special issue aims to collect works on novel robust and data-driven control methods for uncertain learning systems. Works that include topics such as robust learning-based control, robust data-driven control, AI-based learning methods, and other intelligent control topics focusing on the use of these methods are of particular interest.
Potential topics of interest include, but are not limited to, the following:
• Robust design and analysis of learning systems
• Modelling of data-driven learning systems
• Data-driven control
• Adaptive learning control
• Iterative learning control for non-repetitive systems
• Optimization-based learning control
• Distributed learning systems and control
• Observer-based learning systems and control
Schedule
The schedule for this special issue is listed below:
• First Submission Deadline 31 May 2022
• Notification of First Round Decision 31 July 2022
• Revised Paper Submission Deadline 30 September 2022
• Notification of Final Decision 30 November 2022
• Final Paper Submission Deadline 31 December 2022
• Tentative Publication Date Early 2023
To Contribute a Manuscript
The submission site for the special issue is https://mc.manuscriptcentral.com/rnc-wiley. When submitting papers, please choose the special issue titled “Emerging robust and data-driven control methods for uncertain learning systems.”
Special Issue Guest Editors
Deyuan Meng (Corresponding Editor)
School of Automation Science and Electrical Engineering
Beihang University (BUAA), Beijing 100191, China
Emails: dymeng@buaa.edu.cn, dymeng23@126.com
Web: http://shi.buaa.edu.cn/mengdeyuan/zh_CN/index.htm
https://www.researchgate.net/profile/Deyuan-Meng-5
Kevin L. Moore
Division of Engineering, Design, and Society
Department of Electrical Engineering
Colorado School of Mines, Golden, CO 80401, USA
Email: kmoore@mines.edu
Web: http://inside.mines.edu/~kmoore/
Ronghu Chi
School of Automation and Electronic Engineering
Qingdao University of Science and Technology, Qingdao 266061, China
Email: ronghu_chi@hotmail.com
Web: https://zdh.qust.edu.cn/info/1032/1008.htm
https://www.researchgate.net/profile/Ronghu-Chi
