Prof. SHEN's Group
Distributed Artificial Intelligence Laboratory, ERC-FCDE, MoE
School of Mathematics, Renmin University of China

Home    |    Research    |    Honors    |    Teaching    |    Publications    |    Members    |    Future Students    |    ILC DataBase

About us: Distributed Artificial Intelligence Lab (DAI-Lab)

The Distributed Artificial Intelligence Lab (DAI-Lab) was established at the beginning of 2020. The lab consists of three faculties, Prof. Dong Shen, Assoc. Prof. Hao Jiang and Assoc. Prof. Qijiang Song, 1 Postdoctor, 4 PhD candidates, and 14 Master candidates. The laboratory focuses on distributed artificial intelligence and its application in novel intelligent control approaches.

Latest News

[20220418] The paper titled "Iterative Learning Based Consensus Control for Distributed Parameter Type Multi-Agent Differential Inclusion Systems" has been accepted for publication in International Journal of Robust and Nonlinear Control. This work was led by Prof. JinRong Wang.
[20220329] The paper titled "Optimal Learning Control Scheme for Discrete-Time Systems with Nonuniform Trials" has been accepted for publication in IEEE Transactions on Cybernetics. This work was led by Prof. X. Ruan and collaborated with PhD Candidate Chen Liu and Prof. H. Jiang.
[20220313] The paper titled "Adaptive Fixed-Time Antilock Control of Levitation System of High-Speed Maglev Train" has been accepted for publication in
IEEE Transactions on Intelligent Vehicles. This work was led by Prof. H. Xu and collaborated with Mr. T. Zhang and Miss S. Jiang.
[20220220] The paper titled "Enhanced P-type Control: Indirect Adaptive Learning from Set-point Updates" has been accepted for publication in IEEE Transactions on Automatic Control. This work was led by Prof. Ronghu Chi and collaborated with Prof. Zhongsheng Hou and Prof. Biao Huang.
[20220101] We, DAI Lab, wish all of you Happy 2022.

Recent Research Highlight: Learning Control with Fading Channels

Our recent focus is learning control with fading channels. A fading channel indicates the unreliable communication network that the transmitted signal would suffer multiplicative randomness. Such randomness is generally modeled by a random variable with its expectation away from 1; thus, the received signal is usually biased in the sense that its expectation is not the original signal. Consequently, a correction is necessary for the received signal before the following procedures. The primary issue in this direction is how to correct the received signals.
If the statistics information (especially the mean) of the fading channel is known, a direct correction can be made by multiplying the mean inverse to the received signals. The design and analysis are given in [TSMC: Syst-2021]. We notice that the corrected input is involved with large disturbances, which may significantly affect the dynamics of the plant. To address this issue, we proposed an iteration domain moving averaging operator for the received inputs. The results are elaborated in [TNNLS-2020]. Motivated by the averaging idea, we proceed to investigate how the averaging techniques affect the learning and tracking abilities of a conventional learning scheme, where the learning ability is reflected by the convergence rate and tracking ability is reflected by the final tracking precision. To this end, we studied three specific averaging techniques, namely, moving averaging, general average with all historical information, and forgetting-based average. The results demonstrate that the forgetting-based average operator-based scheme can connect the other two schemes by tuning the forgetting factor. The results are provided in [TAC-2021].
If the statistics information is unknown, a promising approach is to estimate the mean of the fading channel. This idea is conducted in [TNNLS-2021a], where an iterative estimation mechanism is proposed using a unit pilot signal in each iteration. This mechanism provides necessary statistical information such that the biased signals after transmission can be corrected before being utilized.
All the above advances assume that the system information is available for the control design. If both system information and fading statistics are unknown, a natural idea is to estimate them simulatenously. To this end, we propose an error transmission mode and an iterative gradient estimation method. Using the faded tracking error data only, the gradient for updating input is iteratively estimated by a random difference technique along the iteration axis. This gradient acts as the updating term of the control signal. The results are summarized in [TNNLS-2021b].

Annual Reports

Annual Report 2020 (pdf, 12M, by request)  A lite version (pdf, 0.7M)
Annual Report 2019 (pdf, 38M, by request)  A lite version (pdf, 0.4M)
Annual Report 2018 (pdf, 28M,
by requestA lite version (pdf, 1M)
Annual Report 2017 (pdf, 17M,
by requestA lite version (pdf, 1M)

Lab Director: Prof. Dong Shen

Research Interests: Iterative learning control, machine learning and its applications, financial mathematics and fintech, stochastic approximation algorithms, multi-agent systems, distributed and decentralized optimization algorithms.

Contact Information

Office Address:
Room 207, No. 4 Teaching Building, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing 100872
Mailing Address:
School of Mathematics, Renmin University of China, No. 59 Zhongguancun Street, Beijing 100872, P.R. China
Tel: 86-10-82507078
E-mail: dshen [at] ieee [dot] org


2005.09-2010.07, Ph.D. in Mathematics, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences
2001.09-2005.07, B.S. in Mathematics, School of Mathematics, Shandong University

Professional Positions

2019.12-present, Professor, School of Mathematics, Renmin University of China
2020.10-present, Head, Department of Information and Computation Sciences, School of Mathematics, RUC
2019.12-present, Principal Investigator, Eng. Res. Center of Finance Computation and Digital Engineering, Ministry of Education
2020.01-present, Director, Distributed Artificial Intelligence Lab., ERC-FCDE, MoE
2019.07-2019.08, Visiting Scholar, RMIT University, Australia
2018.01-2019.12, Professor, Beijing University of Chemical Technology
2012.06-2017.12, Associate Professor, Beijing University of Chemical Technology
2016.02-2017.02, Visiting Scholar, National University of Singapore, Singapore
2010.07-2012.05, Postdoctoral Fellow, Institute of Automation, CAS


Short Biography

Dong SHEN (M'10-SM'17) received the B.S. degree in mathematics from Shandong University, Jinan, China, in 2005. He received the Ph.D. degree in mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), Beijing, China, in 2010 (supervised by Prof. Han-Fu Chen, IEEE Fellow, IFAC Fellow).

From 2010 to 2012, he was a Post-Doctoral Fellow with the Institute of Automation, CAS (advised by Prof. Fei-Yue Wang, IEEE Fellow, IFAC Fellow). From 2016 to 2017, he was a visiting scholar at National University of Singapore (with Prof. Jian-Xin Xu, IEEE Fellow). From 2019 July to August, he was a visiting scholar at RMIT University (with Prof. Xinghuo Yu, IEEE Fellow). From 2012 to 2019, he has been with College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China. Now, he is a Full Professor of Renmin University of China.

His current research interests include iterative learning control, stochastic control and optimization, machine learning and its applications. He has published more than 120 refereed journal and conference papers. He is the (co-)author of Iterative Learning Control for Systems with Iteration-Varying Trial Lengths (Springer, 2019), Iterative Learning Control with Passive Incomplete Information (Springer, 2018), Iterative Learning Control for Multi-Agent Systems Coordination (Wiley, 2017), and Stochastic Iterative Learning Control (Science Press, 2016, in Chinese), and co-editor of Service Science, Management and Engineering: Theory and Applications (Academic Press and Zhejiang University Press, 2012). Dr. Shen received the IEEE CSS Beijing Chapter Young Author Prize in 2014 and the Wentsun Wu Artificial Intelligence Science and Technology Progress Award in 2012. He is an Associate Editor of IEEE Access and IET Cyber-Systems and Robotics.



At Guizhou University, with Prof. Xisheng Dai and Prof. Deyuan Meng, in front of the library
At Nankai University, with Prof. Yuanhua Ni, Prof. Wenxiao Zhao, and Prof. Zengqiang Chen (from left)
At Shandong University, with Prof. Yan Li and his students
At Beihang University, with Prof Jinhu Lv, Prof. Xinghuo Yu, Prof. Hong Li, Prof. Nian Liu (from left)

At Qingdao University of Science and Technology
At Xidian Univerisyt with Prof. Junmin Li, Prof. Xiao'e Ruan, and Prof. Zhengrong Xiang
At Beihang University with Prof. Kevin L. Moore and Prof. Deyuan Meng
At a seaside in Tsingtao with Prof. Deqing Huang (during the 2nd ILC-TableParty 0724)
At DDCLS18 in Enshi with my master students: Mr. Chao Zhang and Mr. Chen Liu
At Whampoa Military Academy, Guangzhou

IEEE Senior Member Certificate
With Koala in Currumbin Widelife Sanctuary, Gold Coast, Australia
IEEE Senior Membership Card
At Great Wild Goose Pagoda in Xi'an
With Miss Yun Xu celebrating her graduation
At DDCLS17 in Chongqing with my master students: Miss Chun Zeng and Mr. Chao Zhang

on Alumni Day at NUS

Mt. Tai-A   Mt. Tai-B

Bachelor Transcript
Receiving President Scholarship from President Tao ZHAN of Shandong University
Letter of Admission from Graduate University of Chinese Academy of Sciences