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

Call for paper

Important date

Draft paper submission deadline
Draft paper acceptance notification
Final paper submission deadline

Submission Topics

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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Important Date
  • Conference Date

    May 12



    May 14


  • Dec 31 2022

    Draft paper submission deadline

  • Mar 15 2023

    Draft Paper Acceptance Notification

  • Apr 15 2023

    Final Paper Deadline

Sponsored By
Beijing Section
Chinese Association of Automation
Hunan University of Science and Technology