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 8th Data Driven Control and Learning Systems Conference (DDCLS'19) will be held in Dali, Yunnan Province, China, on May 24-27, 2019, co-organized by Technical Committee on Data Driven Control, Learning and Optimization, Chinese Association of Automation and Beijing Jiaotong University, technically sponsored by IEEE Beijing Section and IEEE Industrial Electronics Society, and locally organized by Kunming University of Science and Technology and Dali University.
In the last three DDCLS conferences, DDCLS'16 (Yinchuan), DDCLS'17 (Chongqing) and DDCLS'19 (Enshi), a lot of renowned scholars from different countries were invited to deliver keynote addresses, for example, Prof. Steven X. Ding from University of Duisburg-Essen, Prof. H?kan Hjalmarsson from KTH Royal Institute of Technology, Prof. Feng Qian from East China University of Science and Technology, Prof. Marco C. Campi from University of Brescia, Prof. Zhiqiang Gao from Cleveland State University, Prof. Weihua Gui from Central South University, Prof. Frank L. Lewis from University of Texas at Arlington, and Prof. Jian-Xin Xu from National University of Singapore. 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.

The English papers accepted by our previous DDCLS conferences has been included in the IEEE Xplore, and indexed by EI Compendex and ISTP database.

The DDCLS'19 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
-- 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