Emerging Model-Based and Data-Driven Techniques in Control, Communication and Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 1768

Special Issue Editors

Department of Mechanical and Civil Engineering, College of Engineering and Science, Florida Institute of Technology, Melbourne, FL 32901, USA
Interests: control; optimization; machine learning; intelligent transportation systems; power systems

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Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: reinforcement learning for control; neural networks

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Guest Editor
Department of Automatic Control, Robotics and Fluid Technique, Faculty of Mechanical and Civil Engineering, University of Kragujevac, 36000 Kraljevo, Serbia
Interests: stochastic systems; system identification; intelligent systems; fault-tolerant control; machine learning; robotics
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Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
Interests: machine learning; mobile and satellite communication; optimization and control
Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USA
Interests: model predictive control; reinforcement learning; connected and automated vehicles; electric vehicles; renewable energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims at encouraging the discussion and collaboration of researchers in the fields of control, communication, and learning. Traditional techniques in these fields usually rely on system modeling. However, as the models of certain engineering systems become more complex, it is becoming harder, near impossible even, to build a suitable model. In the last two decades, thanks to the dramatic development of data-driven technologies, a promising possibility has been provided in terms of dealing with control, communication, and learning problems using either online or offline data. This Special Issue invites submissions on all topics regarding theory and applications in control, communication, and learning, including but not limited to the following topics described in the keywords.

Dr. Weinan Gao
Dr. Yongliang Yang
Dr. Vladimir Stojanovic
Dr. Di Zhang
Dr. Jun Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • Data-driven study in transportation and driving simulation
  • Data-driven and model-based controller design
  • Applications of data mining and machine learning
  • Path planning, control, and actuation of robots
  • Data-driven and model-based wireless communication

Published Papers (1 paper)

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Research

19 pages, 3796 KiB  
Article
Singularity-Free Fixed-Time Adaptive Control with Dynamic Surface for Strict-Feedback Nonlinear Systems with Input Hysteresis
by Xuxiang Feng, Jun Chen and Tongyao Niu
Electronics 2022, 11(15), 2378; https://doi.org/10.3390/electronics11152378 - 29 Jul 2022
Cited by 1 | Viewed by 1110
Abstract
An adaptive fixed-time dynamic surface tracking control scheme is developed in this paper for a class of strict-feedback nonlinear systems, where the control input is subject to hysteresis dynamics. To deal with the input hysteresis, a compensation filter is introduced, reducing the difficulty [...] Read more.
An adaptive fixed-time dynamic surface tracking control scheme is developed in this paper for a class of strict-feedback nonlinear systems, where the control input is subject to hysteresis dynamics. To deal with the input hysteresis, a compensation filter is introduced, reducing the difficulty of design and analysis. Based on the universal approximation theory, the radial basis function neural networks are employed to approximate the unknown functions in the nonlinear dynamics. On this basis, fixed-time adaptive laws are constructed to approximate the unknown parameters. The dynamic surface technique is utilized to handle the complexity explosion problem, where fixed-time performance is ensured. Moreover, the designed controller can avoid singularities and achieve fixed-time convergence of error signals. Simulation results verify the efficacy of the method developed, where a comparison between the scheme developed with existing results is provided. Full article
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