Artificial Intelligence Laboratory, ERC-FCDE, MoE
School of Mathematics, Renmin
University of China
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About us: Distributed Artificial Intelligence 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, 6 PhD
candidates, and 17 Master
candidates. The laboratory focuses on distributed artificial
and its application in novel intelligent control approaches.
 The paper titled "Iterative
Learning Based Consensus Control for Distributed Parameter Type
Multi-Agent Differential Inclusion Systems With Time-Delay" has been accepted for publication in Computers & Mathematics with Applications. This work was led by Prof. Wang.
 The paper titled "Iterative Learning Control for Conformable Stochastic Impulsive Differential Systems With Randomly Varying Trial Lengths" has been accepted for publication in International Journal of Nonlinear Sciences and Numerical Simulation. This work was led by Prof. Wang.
The paper titled "Finite-Level Uniformly Quantized Learning Control
with Random Data Dropouts" has been accepted for publication in International Journal of Robust and Nonlinear Control. This work was collaborated with Ms. Huo, Prof. Jiang, and Prof. Wang.
 The paper titled "A Novel Adaptive Gain Strategy for Stochastic Learning Control" has been accepted for publication in IEEE Transactions on Cybernetics as a regular paper. This work was collaborated with Mr. Cheng, Prof. Jiang, and Prof. Yu.
 The paper titled "Distributed Iterative Learning Temperature Control for Large-Scale Buildings" has been accepted for publication in International Journal of Robust and Nonlinear Control. This work was collaborated with Mr. He.
 The paper titled "Practical Learning-Tracking Framework Under Unknown Nonrepetitive Channel Randomness" has been accepted for publication in IEEE Transactions on Automatic Control as a regular paper.
 The paper titled "Accelerated Learning Control for Point-to-Point Tracking Systems" has been accepted for publication in IEEE Transactions on Neural Networks and Learning Systems. This work was collaborated with Prof. Jiang, Mr. Huang, and Prof. Yu.
 The paper titled
"Nonlinear Robust Composite Levitation Control for High-Speed EMS
Trains with Input Saturation and Track Irregularities" has been
accepted for publication in IEEE Transactions on Intelligent
Transportation Systems. This work was collaborated with Dr. Jiang, Dr.
Zhang, and Prof. Hongze Xu from Beijing Jiaotong University.
 The paper titled
"A High-Order Norm-Product Regularized
Multiple Kernel Learning Framework for Kernel Optimization" has
been accepted for publication in Information
Sciences. This work was led by Prof. Hao Jiang.
to Yiyao Dou and Yifan Feng, who has successfully obtained their Master
 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.
 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.
 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.
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.
 We, DAI Lab,
of you Happy 2022.
Highlight: Learning Control with
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:
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].
In addition, we have successfully extended the learning control scheme
to a distributed version for multi-agent systems, where each agent is
controlled by the input signal generated using the faded neighborhood
information (FDI). The iterationwise asymptotic consensus tracking is
strictly established for both linear and affine nonlinear multi-agent
systems. The results are provided in [TNNLS-2021c]
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].
Furthermore, we consider the general case that the fading channels' statistics, i.e., mean and covariance, are nonrepetitive in the iteration domain.
This nonrepetitive randomness introduces non-stationary contamination
and drifts to the actual signals, yielding essential challenges in
signal processing and learning control. we propose a practical
framework constituted by an unbiased estimator of the mean inverse, a
signal correction mechanism, and learning control schemes. The
convergence and tracking performance are established for both constant
and decreasing step-lengths. If the statistics satisfy asymptotic repetitiveness
in the iteration domain, a consistent estimator applies to the
framework while retaining the framework's asymptotic properties. These
results are demonstrated in [TAC-2022].
All the above publications are regular papers in IEEE Transactions. For a complete list, please refer to the LazyPack under the Topic: ILC with fading channels.
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.
learning control, machine learning and its applications, financial
mathematics and fintech, stochastic
multi-agent systems, distributed
and decentralized optimization algorithms.
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/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/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 with the School of Mathematics
, Renmin University of China
, Beijing, China.
His current research interests include iterative learning control,
stochastic control and optimization, machine learning and its
applications. He has published more than 140
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 Shandong Provincial Natural Science Award in 2021, IEEE
CSS Beijing Chapter Young Author Prize in 2014
, and Wentsun Wu
Artificial Intelligence Science and Technology Progress Award in 2012.
He is on the Editorial Board of International Journal of Robust and Nonlinear Control, IEEE Access
and IET Cyber-Systems and Robotics.
At Le Méridien Emei Mountain Resort, DDCLS2022, with Prof. Hao Jiang, Prof. Na Dong, and Prof. Fuyong Wang
At DDCLS2021, Suzhou, presenting the invited talk Another pic
At Renmin University of China, with my students Mr. Yanze Liu, Mr. Kun Zeng, Mr. Ganggui Qu, and Ms Niu Huo
At Renmin University of China, with school leaders and Department of Information and Computation Sciences
At Renmin University of China, speech as the department head
At YAC2020，with Prof. Bo Zhao presenting my certificate
At Sun Yat-sen University, a talk for Prof Xiaodong Li's Team
University, with Prof. Xisheng Dai and Prof. Deyuan Meng, in front of
At Guizhou University, with Prof. JinRong and his team, Prof. Deyuan Meng, Prof. Xisheng Dai
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