The 6th Data Driven Control and Learning Systems Conference 2017 DDCLS

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Data Driven Control and Learning Systems Conference (DDCLS) is an annual conference organized by Technical Committee on Data Driven Control, Learning and Optimization, Chinese Association of Automation. The 6th Data Driven Control and Learning Systems Conference (DDCLS'17) will be held in Chongqing, China, May 26-27, 2017, sponsored by IEEE Beijing Section and IEEE Industrial Electronics Society, and locally organized by Chongqing Jiaotong University.

In the last two DDCLS conferences, DDCLS’15, Beijing and DDCLS’16, Yinchuan, a lot of renowned scholars from different countries were invited to deliver plenary lectures, for example, Prof. Frank L. Lewis from University of Texas at Arlington, Prof. Jian-Xin Xu from National University of Singapore, Prof. Tianyou Chai from Northeastern University and Prof. Feiyue Wang from Chinese Academy of Sciences. DDCLS’s purpose is to create a forum for scientists, engineers and practitioners throughout the world to present the latest developments in data-driven control, learning, automation and optimization.

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2016-12-30

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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
Reinforcement learning control
Robustness on data-driven control
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 of complex big-data systems and applications
Big data in industrial processes and its applications in modeling and control