Deep Learning and Adaptive Control, 4th 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 amount 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 increasingly 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.

This Special Issue on the research progress in deep learning will help 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

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