活动简介

Data-driven control and learning has been developed quickly both in theory and applications recently. The deep involvement of information science in practical processes poses enormous challenges to the existing control science and engineering due to their size, distributed nature and complexity. Modeling these processes accurately using first principles or identification is almost impossible although these plants produce huge amount of operation data in every moment. The high-tech hardware/software and the cloud computing enable us to perform complex real-time computation, which makes implementation of data-driven control and method for these complex practical plants possible. It would be very significant if we can learn the systems' behaviors, discover the relationship of system variables by making full use of on-line or off-line process data, to directly design controller, predict and assess system states, make decisions, perform real-time optimization and conduct fault diagnosis.
Sponsor Type:3; 9; 9

征稿信息

重要日期

2022-12-31
初稿截稿日期
2023-03-15
初稿录用日期
2023-04-15
终稿截稿日期

征稿范围

The English papers accepted by our previous DDCLS conferences had been included in the IEEE Xplore, and indexed by EI Compendex or ISTP database. The DDCLS’23 covers both theory and applications in all the areas of data driven control and learning systems. The topics of interest include, but are not limited to:

  • Data-driven control theory, approaches and applications 
  • Model-free adaptive control theory and applications 
  • Active disturbance rejection control and applications 
  • Data-driven fault diagnosis, health maintenance and performance evaluation 
  • Iterative learning identification, iterative learning control(repetitive control) 
  • Data-driven modeling, optimization, scheduling, decision and simulation 
  • Statistical learning, machine learning, data mining and practical applications in automation field 
  • Neural networks, fuzzy systems control methods in data driven manner 
  • Adaptive dynamic programming, reinforcement learning and learning based control 
  • Robustness on data-driven control 
  • Relationships between data-driven and model-based control methods 
  • Complementary controller design approaches and relationships between data-driven and model-based control methods 
  • Applications of data-driven methods to industrial processes
  • Data-driven modeling, control and optimization for traffic systems 
  • Data-driven control for practical complex processes
  • Technology and applications of complex big-data systems 
  • Big data in industrial processes and its applications in modeling and control 
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重要日期
  • 会议日期

    05月12日

    2023

    05月14日

    2023

  • 12月31日 2022

    初稿截稿日期

  • 03月15日 2023

    初稿录用通知日期

  • 04月15日 2023

    终稿截稿日期

  • 05月14日 2023

    注册截止日期

主办单位
Beijing Section
Chinese Association of Automation
Hunan University of Science and Technology
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