Deep Learning and Adaptive Control, 3rd Edition

Dear Colleagues,

Deep learning is a research hotspot in artificial intelligence, machine learning, and data science. It has made many achievements in search technology, machine learning, machine translation, natural language processing, and other related fields. The applications of deep learning are undoubtedly worthy of attention. Recent results in deep learning have left no doubt that it is amongst the most powerful modeling and control tools that we possess. The real question is how we can utilize deep learning for control without losing stability and performance guarantees. At present, with the increasing number of data to be processed, the calculation process is more complex and cumbersome than before, and the efficiency of the algorithm may be reduced due to overfitting. As the models become more and more complex, their interpretability will be reduced, and the performance as well as efficacy of the algorithms will be reduced accordingly, which requires further research. Even though recent successes in deep reinforcement learning (DRL) have shown that deep learning can be a powerful value function approximator, several key questions must be answered before deep learning enables a new frontier in unmanned systems.

The Special Issue on the research progress in deep learning will help to update the most advanced methods, technologies, and applications in this field. DRL is closely tied theoretically to adaptive control. Recent work has shown how to use DRL to develop new forms of adaptive controllers that effectively deal with some existing open problems in adaptive control, such as handling unmatched uncertainties. Any actual system has varying degrees of uncertainty. When facing the changes in internal characteristics and the influence of external disturbances, it is necessary to adopt adaptive control. Since its first development, adaptive control has been keeping pace with the development of science and engineering, and more new methods as well as applications have been introduced over time. This Special Issue aims to introduce the latest progress in adaptive control theory and application. The key points are system modeling, parameter identification, structural analysis, controller design, performance analysis, and the application research results of adaptive control algorithms. We are looking for the latest research results in deep learning and adaptive control. Topics of interest include, but are not limited to, the keywords listed below.

Prof. Dr. Zhijia Zhao
Prof. Dr. Zhijie 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 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. Mathematics 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 2600 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.

• deep learning, CNN, RNN, transformer model
• optimization of deep learning
• applications of deep learning
• reinforcement-learning-based control
• applications of reinforcement learning
• adaptive iterative learning control
• modeling of adaptive systems
• design of adaptive controllers
• application of adaptive control

• Deep Learning and Adaptive Control in Mathematics (13 articles)