DAI Lab@RUC

[20220908] 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.
[20220714] 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.
[20220615] 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.
[20220615] 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.
[20220612] 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.
[20220522] 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.
[20220516] 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.
[20220515] Congratulations to Yiyao Dou and Yifan Feng, who has successfully obtained their Master Degrees.
[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.

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]. 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.

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 140 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 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.