DDCLO

Conference Information

Data Driven Control and Learning Systems Conference is an annual conference organized by Technical Committee on Data Driven Control, Learning and Optimization, Chinese Association of Automation. The conference, consisting of keynotes, plenary lectures, regular sessions and invited/special sessions, provides a forum for scientists, engineers and practitioners throughout the world to present their latest theoretical results and techniques in the field of data-driven control, learning, automation and optimization.

Data driven control and learning system is an emerging and hot research area in the field of automation as an indispensable part of complete control theory besides model-based control. It focuses on control, optimization and learning for the plants when their models are unavailable. Although the study on data driven control and learning is still in the embryonic stage, it has attracted a great deal of attentions within the control theory community. In recent years, there are several special issues published (or to be published) in the top journals of control community: ACTA AUTOMATICA SINICA (2009), IEEE Transactions on Neural Networks (2011), Information Sciences (2011), IEEE Transactions on Industrial Informatics (2013), IEEE Transactions on Industrial Electronics (2015, 2016), IET Control Theory & Applications (2015, 2016).

Following the data-based trend in control community and meeting practical requirements by large-scale industrial enterprises, the Technical Committee on Data Driven Control, Learning and Optimization was established under supervision of Chinese Association of Automation in Beijing, June 2015. The purpose of this technical committee is to promote development and prosperity of data-driven control, learning and optimization both in theory and applications in China.

In fact, there was a series of workshops in Chinese community of data driven control and learning from 2006, called as “Iterative Learning Control Symposium” including ILC’2006 in Hangzhou, ILC’2012 in Xi’an, ILC’2014 in Qiangdao, and two informal workshops at 2007 CCC in Zhangjiajie and 2010 CCC in Beijing. After the establishment of the Technical Committee on Data Driven Control, Learning and Optimization, the symposium was renamed the forth Data Driven Control and Learning System Conference (DDCLS’15), in order to reflect its changes and expansion as well as to keep its continuity and history.

In May 2016, The 5th Data Driven Control and Learning Systems Conference (DDCLS'16) was hold by the technical committee in Yinchuan, and received an enthusiastic response with a total of 128 submissions. After going through a rigorous review process, 102 papers were accepted and presented in ten oral sessions and one interactive sessions during the conference. 172 delegates from different countries/regions attended the conference. In addition to the normal technical sessions, the technical program also included three keynote speeches and six plenary talks covering the state-of-the-art in data-driven control and learning.

In the past conferences, a lot of renowned scholars were invited to deliver keynote addresses or plenary lectures, for example, Prof. Jian-Xin Xu from National University of Singapore (ILC’2006 and DDCLS'16), Prof. Zeungnam Bien from Ulsan National Institute of Science and Technology (ILC’2012), Prof. Danwei Wang from Nanyang Technological University (ILC’2012), Prof. Furong Gao from Hong Kong University of Science and Technology (ILC’2014), Prof. Chiang-Ju Chien from Huafan University (ILC’2014), Academician Tianyou Chai from Northeastern University (DDCLS’15), Prof. Feiyue Wang from Chinese Academy of Sciences (DDCLS’15), Prof. Chenghong Wang from National Natural Science Foundation of China (DDCLS’15), Prof. Frank L. Lewis from University of Texas at Arlington (DDCLS’16) and Prof. Qunxiong Zhu from Beijing University of Chemical and Technology (DDCLS’16).

It is our target that Data Driven Control and Learning Systems Conference will become a formal, large-scaled, international conference with great academic influence in the community of data driven control and learning in the future.