Data-Based Learning Methods and Their Applications

Dear Colleagues,

Data-based learning methods are an important and hot research direction in modern control theory and pattern recognition, among other fields. In contrast to traditional control techniques, data-based learning control methods require less information about system dynamics and use collected and stored data to construct the controllers or the control inputs and to discover underlying patterns; these methods have demonstrated superior performance. Despite the success of data-based learning methods for repetitive or non-strict repetitive control systems, pattern recognition, reinforcement learning, etc., data-based learning paradigms and their applications are still lacking.

This Special Issue aims to collect works on novel data-driven methods and their applications for repetitive or non-strict repetitive control systems. Works that include topics such as the design and analysis of iterative learning control systems, data-driven learning control techniques, non-standard iterative learning control, reinforcement learning, pattern recognition, and other learning control topics based on data are of particular interest.

Potential topics of interest include, but are not limited to, the following:

• Design and analysis of iterative learning control systems;
• Networked iterative learning control systems;
• Data-driven control techniques;
• Non-standard iterative learning control;
• Optimization iterative learning control;
• Iterative learning control for large-scale systems;
• Robust iterative learning control;
• Reinforcement learning, pattern recognition and their applications.

Prof. Dr. Yuanshi Zheng
Dr. Jian Liu
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 250 words) can be sent to the Editorial Office for assessment.

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.