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, 6 PhD candidates, and 17 Master candidates. The laboratory focuses on distributed artificial intelligence and its application in novel intelligent control approaches.

Latest News

[20230330] The paper titled "Learning ability analysis for linear discrete delay systems with iteration-varying trial length" has been accepted for publication in top journal Chaos, Solitons & Fractals. This work was led by Prof. JinRong Wang.
[20230314] The paper titled "Distributed Learning Control for High-Speed Trains With Operation Safety Constraints" has been accepted for publication in IEEE Transactions on Cybernetics. This work was collaborated with Mr. Gao and Prof. Song.
[20230223] The paper titled "Enhancing Iterative Learning Control With Fractional Power Update Law" has been accepted for publication in IEEE/CAA Journal of Automatica Sinica. This work was collaborated with Mr. Li and Prof. Yu.
[20230112] The Annual Report of DAI Lab has been released. Please click here for the PDF file.
[20230109] The manuscript titled "Adaptive Finite-Time Fuzzy Control for Hybrid Levitation System of Maglev Trains With Active Anti-lock Constraints" has been accepted for publication in Journal of the Franklin Institute. This work was led by Dr. Zhang.
[20230101] The manuscript titled "Fixed-time Adaptive Consensus for Stochastic Multi-Agent Systems with Uncertain Actuator Faults" has been accepted for publication in ISA Transactions. This work was led by Prof. Wang.
[20230101] We, DAI Lab, wish you Happy 2023.

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.

icon 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]. 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]

icon 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].

icon 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 Reports

Annual Report 2022  (pdf, 2.5M)  NEW
Annual Report 2020  (pdf, 0.7M)
Annual Report 2019  (pdf, 0.4M)
Annual Report 2018 
(pdf, 1M)
Annual Report 2017 
  (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/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 has been with College of Information Science and Technology, Beijing University of Chemical Technology, 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 160 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). Dr. Shen received Henan Provincial Natural Science Award in 2022, 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

At Guizhou University, with Prof. Xisheng Dai and Prof. Deyuan Meng, in front of the library
At Guizhou University, with Prof. JinRong and his team, Prof. Deyuan Meng, Prof. Xisheng Dai
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