DAI Lab@RUC

[20241110] The manuscript titled "Encoding-Decoding-Based Quantized Learning Control Using Spherical Polar Coordinates" has been accepted for publication in IEEE Transactions on Cybernetics as a regular paper.
[20241029] The manuscript titled "Finite and Fixed-Time Learning Control for Continuous-Time Nonlinear Systems" has been accepted for publication in IEEE Transactions on Systems, Man, and Cybernetics: Systems as a regular paper.
[20241029] The manuscript titled "Accelerating Iterative Learning Control Using Fractional-Proportional-type Update Rule" has been accepted for publication in IEEE Transactions on Automatic Control as a technical note.
[20241023] The manuscript titled "Continuous Discrete Optimal Transportation Particle Filter" has been accepted for publication in The Asian Journal of Mathematics. This work was led by Assit. Prof. Xiuqiong Chen.
[20241018] Prof. Wai-Ki Ching from Department of Mathematics, The University of Hong Kong deliver a speed with title "On Adaptive Online Mean-Variance Portfolio Selection Problems".
[20241016] Prof. Rajiv Ranjan from School of Computing of Newcastle University, United Kingdom, deliver a speech on "Data and Resource Management Challenges for Digital Twins".
[20240926] The manuscript titled "A Uniform Framework of Yau-Yau Algorithm Based on Deep Learning with the Capability of Overcoming the Curse of Dimensionality" has been accepted for publication in the top journal IEEE Transactions on Automatic Control as a full paper. This work was led by Assit. Prof. Xiuqiong Chen.
[20240917] The manuscript titled "Synthesis of Safety and Ride Comfort Control for Chassis of Maglev Trains" has been accepted for publication in IEEE Transactions on Intelligent Transportation Systems. This work was led by Prof. You.
[20240915] The manuscript titled "Decentralized Learning Control for High-Speed Trains With Unknown Time-Varying Speed Delays" has been accepted for publication in Applied Mathematical Modelling.
[20240817] The manuscript titled "Multi-View Data Clustering via Dynamical Optimization of Consensus Laplacian Matrix" has been accepted for publication in East Asian Journal on Applied Mathematics.
[20240814] The manuscript titled "Co-regularized Optimal High-Order Graph Embedding for Multi-View Clustering" has been accepted for publication in top journal Pattern Recognition.
[20240805] The manuscript titled "An Accelerated Adaptive Gain Design in Stochastic Learning Control" has been accepted for publication in IEEE Transactions on Cybernetics.
[20240731] Congratulations! The manuscript "Fast iterative learning control algorithms based on heavy ball with adaptive stepsize", which was accpeted by CCC2024, won 2024 SCIS-CCC The Honorable Mention of Poster Paper Award.
[20240727] The manuscript titled "Zero-Error Tracking Control of Quantized Iterative Learning for Differential Inclusion Systems with Channel Fading" has been accepted for publication in Nonlinear Dynamics. This work was led by Prof. Jinrong Wang.
[20240718] Congratulations! Our faculty member Jiang Hao has been promoted to be a Full Professor.
[20240716] Congratulations! Our paper "Data-driven learning control algorithms meeting unachievable tracking problems", authored by Zeyi Zhang, Hao Jiang, Dong Shen, and Samer S. Saab, has been indexed as a Highly Cited Paper in Web of Science.
[20240629] The manuscript "A Uniform Framework of Yau-Yau Algorithm Based on Deep Learning with the Capability of Overcoming the Curse of Dimensionality" has been accepted for publication in IEEE Transactions on Automatic Control. This work was led by Assistant Prof. Xiuqiong Chen.
[20240527] The paper "Observer-based sampled-data event-triggered tracking for nonlinear multi-agent systems with semi-Markovian switching topologies" has been accepted for publication in Information Sciences. This work was led by Prof. Wang.
[20240519] Congratulations! Our paper "Iteration number bound for adaptive learning control of nonlinear systems under specified approximation error" with Dunsheng Huang as the first author has won the Best Poster Paper Award of the 2024 IEEE 13th Data Driven Control and Learning Systems Conference.
[20240513] Congratulations! Our paper "Accelerated learning control for point-to-point tracking systems", authored by Hao Jiang, Dong Shen, Shunhao Huang, and Xinghuo Yu, has been indexed as a Highly Cited Paper in Web of Science.
[20240507] Congratulations! Zihan Li has won the Wu Yuzhang Scholarship Finalist Award.
[20240507] The manuscript "Novel Quantized Iterative Learning Control Based on Spherical Polar Coordinates" has been accepted for publication in International Journal of Robust and Nonlinear Control.
[20240419] The paper "scTPC: a novel semi-supervised deep clustering model for scRNA-seq data" has been accepted for publication in Bioinformatics.
[20240417] The paper "scEWE: High-order Element-wise Weighted Ensemble Clustering for heterogeneity analysis of single cell RNA-sequencing data" has been accepted for publication in Briefings in Bioinformatics. This work was led by Assoc. Prof. Hao Jiang.
[20240415] The manuscript "Time-varying Feedback Particle Filter" has been accepted for publication in Automatica. This work was led by Assistant Prof. Xiuqiong Chen.
[20240320] Congratulations! Our paper Practical learning-tracking framework under unknown nonrepetitive channel randomness won The 17th Beijing Youth Outstanding Scientific and Technological Paper Award.
[20240307] The manuscript "Iterative Learning based Convergence Analysis for Nonlinear Impulsive Differential Inclusion Systems with Randomly Varying Trial Lengths" has been accepted for publication in International Journal of Adaptive Control and Signal Processing. This work was led by Prof. Wang.
[20240224] The manuscript "Data-Driven Distributed Learning Control for High-Speed Trains Considering Quantization Effects and Measurement Bias" has been accepted for publication in IEEE Transactions on Vehicular Technology. This work was led by Prof. Deqing Huang.
[20240222] The manuscript "A Multistage Update Rule Framework for Iterative Learning Control Systems" has been accepted for publication in IEEE Transactions on Automation Science and Engineering. This work was collaborated with Prof. Yu.
[20240131] The manuscript "Iterative Learning Control for Differential Inclusion Systems with Random Fading Channels by Varying Average Technique" has been accepted for publication in Chaos. This work was led by Prof. Wang.
[20240103] The DAI Lab Annual Report for 2023 has been released.
[20240101] We, DAI Lab, wish you Happy 2024.

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.