Artificial Intelligence Laboratory, ERC-FCDE, MoE
School of Mathematics, Renmin
University of China
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Brief of Distributed Artificial Intelligence Lab (DAI-Lab)
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, 3 PhD candidates, and 13 Master
candidates. The laboratory focuses on distributed artificial intelligence
and its application in novel intelligent control approaches.
 Congratulations to Ganggui Qu and Niu Huo, who have obtained their Master Degree in Engineering.
Our papge "Learning Tracking Control Over Unknown Fading Channels
Without System Information" has been officially assigned in an issue of
IEEE Transactions on Neural Networks and Learning Systems (vol. 32, no. 6, pp. 2721-2732).
Our paper "Averaging Techniques for Balancing Learning and Tracking
Abilities Over Fading Channels" has been offcially assigned in an issue
of IEEE Transactions on Automatic Control (vol. 66, no. 6, pp. 2636-2651).
 Our manuscript, titled "A Probabilistically Quantized Learning Control Framework for Networked Linear Systems", has been accepted for publication in IEEE Transactions on Neural Networks and Learning Systems.
Our manuscript, titled "Convergence Analysis for Iterative Learning
Control of Impulsive Linear Discrete Delay Systems", has been accepted
for publication in Journal of Difference Equations and Applications. This worked was led by Prof. Jinrong Wang.
 Prof. Shen has won the First Prize of the 11th Teaching Competition for Young Teachers at RUC.
Our manuscript, titled "Learning Control for Networked Stochastic
Systems With Random Fading Communication", has been accepted for
publication in IEEE Transactions on Systems, Man, and Cybernetics-Systems. This work is collaborated with Mr. Ganggui Qu and Assoc. Prof. Qijiang Song.
Our manuscript, titled "An Efficient Algorithm for Collaborative
Learning Model Predictive Control of Nonlinear Systems", has been
accepted for publication in ISA Transactions. This work is collaborated with Mr. Yanze Liu.
Our manuscript, titled "Iterative Learning Control for Output Tracking
of Nonlinear Systems With Unavailable State Information", has been
accepted for publication in IEEE Transactions on Neural Networks and Learning Systems. This work is led by Prof. Xuefang Li and collaborated with Prof. Beichen Ding from Sun Yat-Sen University.
Our manuscript, titled "Iterative Learning Control: Practical
Implementation and Automation", has been accepted for publication in IEEE Transactions on Industrial Electronics. This work is collaborated with Prof. Samer Saab.
The paper "Iterative Learning Tracking for Multi-Sensor Systems: A
Weighted Optimization Approach" has been assigned in an issue of IEEE Transactions on Cybernetics (vol. 51, no. 3, pp. 1286-1299).
The paper "An Iterative Learning Control Algorithm with Gain Adaptation
for Stochastic Systems" wins Outstanding Research Achievement Award of
Renmin University of China. [PaperLink][NewsCover]
 The paper "Iterative
Learning Control for Multi-Agent Systems with Impulsive Consensus
been assigned in an issue of Nonlinear
Analysis: Modelling and Control. [WebLink]
 Our annual
report for 2020 has been released.
 We, DAI Lab,
of you Happy 2021.
Recent Research Highlight: Learning Control with Fading Channels
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 Report 2020 (pdf, 12M, by
request) A lite version (pdf, 0.7M)
Report 2019 (pdf, 38M, by request) A lite
version (pdf, 0.4M)
Report 2018 (pdf, 28M, by request) A lite
version (pdf, 1M)
Report 2017 (pdf, 17M, by request) A lite
version (pdf, 1M)
Lab Director: Prof. Dong Shen
Room 207, No. 4
Teaching Building, Renmin University of China, No. 59
Zhongguancun Street, Haidian District, Beijing 100872
Mathematics, Renmin University of
No. 59 Zhongguancun Street, Beijing 100872, P.R. China
E-mail: dshen [at] ieee [dot] org
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
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.,
2019.07-2019.08, Visiting Scholar, RMIT University, Australia
Professor, Beijing University of Chemical Technology
Associate Professor, Beijing University of Chemical Technology
2016.02-2017.02, Visiting Scholar, National University of Singapore,
2010.07-2012.05, Postdoctoral Fellow, Institute of Automation, CAS
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
, 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
with College of
Information Science and Technology
, Beijing University of
, 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
(Springer, 2019), Iterative
Learning Control with Passive Incomplete Information
2018), Iterative Learning
Control for Multi-Agent Systems Coordination
(Wiley, 2017), and Stochastic Iterative Learning
(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.
University, with Prof. Xisheng Dai and Prof. Deyuan Meng, in front of
University, with Prof. Yuanhua Ni, Prof. Wenxiao Zhao, and Prof.
Chen (from left)
University, with Prof. Yan Li and his students
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
Senior Member Certificate
With Koala in
Currumbin Widelife Sanctuary, Gold Coast, Australia
Senior Membership Card
At Great Wild
Goose Pagoda in
With Miss Yun Xu
in Chongqing with
my master students: Miss Chun Zeng and Mr. Chao Zhang
on Alumni Day at NUS
Mt. Tai-A Mt. Tai-B
President Scholarship from President Tao ZHAN of Shandong University
Admission from Graduate University of Chinese Academy of Sciences