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Center of Intelligent and Learning Systems
Prof. Dong SHEN's Group
College of Information Science and Technology, Beijing University of Chemical Technology

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= Kind Note = This lazy package aims to facilitate references of our publications. Click here for the complete list.

Topic: ILC Surveys
  1. Dong Shen. A Technical Overview of Recent Progresses on Stochastic Iterative Learning Control. Unmanned Systems, vol. 6, no. 3, pp. 147-164, 2018. [PDF][WebLink]
  2. Dong Shen. Iterative Learning Control with Incomplete Information: A Survey. IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 5, pp. 885-901, 2018. [PDF]
  3. Dong Shen, Youqing Wang. Survey on Stochastic Iterative Learning Control. Journal of Process Control, vol. 24, no. 12, pp. 64-77, 2014. [PDF]
Topic: ILC with Random Data Dropouts
  1. Dong Shen. Data-Driven Learning Control for Stochastic Nonlinear Systems: Multiple Communication Constraints and Limited Storage. IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2429-2440, 2018. [PDF] TOP, REGULAR PAPER
  2. Yanqiong Jin, Dong Shen. Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides. Asian Journal of Control, vol. 20, no. 4, pp. 1624-1636, 2018. [PDF]
  3. Dong Shen, Chao Zhang, Yun Xu. Intermittent and Successive ILC for Stochastic Nonlinear Systems with Random Data Dropouts. Asian Journal of Control, vol. 20, no. 3, pp. 1102-1114, 2018. [PDF]
  4. Dong Shen, Jian-Xin Xu. A Framework of Iterative Learning Control under Random Data Dropouts: Mean Square and Almost Sure Convergence. International Journal of Adaptive Control and Signal Processing, vol. 31, no. 12, pp. 1825-1852, 2017. [PDF]
  5. Dong Shen, Jian-Xin Xu. A Novel Markov Chain Based ILC Analysis for Linear Stochastic Systems Under General Data Dropouts Environments. IEEE Transactions on Automatic Control, vol. 62, no. 11, pp. 5850-5857, 2017. [PDF] TOP
  6. Dong Shen, Yanqiong Jin, Yun Xu. Learning Control for Linear Systems under General Data Dropouts at Both Measurement and Actuator Sides: A Markov Chain Approach. Journal of the Franklin Institute, vol. 354, no. 13, pp. 5091-5109, 2017. [PDF]
  7. Dong Shen. Almost Sure Convergence of ILC for Networked Linear Systems with Random Link Failures. International Journal of Control, Automation, and Systems, vol. 15, no. 2, pp. 647-655, 2017. [PDF]
  8. Dong Shen, Chao Zhang, Yun Xu. Two Updating Schemes of Iterative Learning Control for Networked Control Systems with Random Data Dropouts. Information Sciences, vol. 381, pp. 352-370, 2017. [PDF]
  9. Dong Shen, Youqing Wang. ILC for Networked Nonlinear Systems with Unknown Control Direction Through Random Lossy Channel. System & Control Letters, vol. 77, pp. 30-39, 2015. [PDF]
  10. Dong Shen, Youqing Wang. Iterative Learning Control for Networked Stochastic Systems with Random Packet Losses. International Journal of Control, vol. 88, no. 5, pp. 959-968, 2015. [PDF]
Topic: ILC with Random Varying Lengths
  1. Chen Liu, Dong Shen, Jinrong Wang. Adaptive Learning Control for General Nonlinear Systems With Nonuniform Trial Lengths, Initial State Deviation, and Unknown Control Direction. International Journal of Robust and Nonlinear Control, vol. 29, no. 17, pp. 6227-6243, 2019. DOI: 10.1002/rnc.4718 [PDF][WebLink] TOP
  2. Dong Shen, Jian-Xin Xu. Adaptive Learning Control for Nonlinear Systems with Randomly Varying Iteration Lengths. IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 4, pp. 1119-1132, 2019. [PDF][WebLink] TOP, REGULAR PAPER
  3. Dong Shen, Jian-Xin Xu. Robust Learning Control for Nonlinear Systems with Nonparametric Uncertainties and Non-uniform Trial Lengths. International Journal of Robust and Nonlinear Control, vol. 29, no. 5, pp. 1302-1324, 2019. [PDF][WebLink] TOP
  4. Chun Zeng, Dong Shen, JinRong Wang. Adaptive Learning Tracking for Robot Manipulators with Varying Trial Lengths. Journal of the Franklin Institute, vol. 356, no. 12, pp. 5993-6014, 2019. DOI:10.1016/j.jfranklin.2019.04.034 [PDF][WebLink] TOP
  5. Chun Zeng, Dong Shen. JinRong Wang. Adaptive Learning Tracking for Uncertain Systems with Partial Structure Information and Varying Trial Lengths. Journal of the Franklin Institute, vol. 355, no. 15, pp. 7027-7055, 2018. [PDF][WebLink]
  6. Lanjing Wang, Xuefang Li, Dong Shen. Sampled-data Iterative Learning Control for Continuous-time Nonlinear Systems with Iteration-Varying Lengths. International Journal of Robust and Nonlinear Control, vol. 28, no. 8, pp. 3073-3091, 2018. [PDF] TOP
  7. Xuefang Li, Dong Shen. Two Novel Iterative Learning Control Schemes for Systems with Randomly Varying Trial Lengths. Systems & Control Letters, vol. 107, pp. 9-16, 2017. [PDF]
  8. Dong Shen, Wei Zhang, Jian-Xin Xu. Iterative Learning Control for Discrete Nonlinear Systems with Randomly Iteration Varying Lengths. Systems & Control Letters, vol. 96, pp. 81-87, 2016. [PDF]
  9. Dong Shen, Wei Zhang, Youqing Wang, Chiang-Ju Chien. On Almost Sure and Mean Square Convergence of P-type ILC Under Randomly Varying Iteration Lengths. Automatica, vol. 63, no. 1, pp. 359-365, 2016. [PDF] TOP
Topic: ILC with Fading Channels
  1. Dong Shen, Ganggui Qu. Performance Enhancement of Learning Tracking Systems Over Fading Channels with Multiplicative and Additive Randomness. IEEE Transactions on Neural Networks and Learning Systems. DOI: 10.1109/TNNLS.2019.2919510 [WebLink] TOP, REGULAR PAPER
  2. Ganggui Qu, Dong Shen. Stochastic Iterative Learning Control With Faded Signals. IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 5, pp. 1196-1208, 2019. DOI: 10.1109/JAS.2019.1911696 [PDF][WebLink]
Topic: ILC with Quantization
  1. Niu Huo, Dong Shen. Improving Boundary Level Calculation in Quantized Iterative Learning Control with Encoding and Decoding Mechanism. IEEE Access, vol. 7, no. 1, pp. 66623-66632, 2019. [PDF][WebLink]
  2. Chao Zhang, Dong Shen. Zero-Error Convergence of Iterative Learning Control Based on Uniform Quantisation with Encoding and Decoding Mechanism. IET Control Theory & Applications, vol. 12, no. 14, pp. 1907-1915, 2018. [PDF][WebLink]
  3. Yun Xu, Dong Shen, Xuhui Bu. Zero-Error Convergence of Iterative Learning Control Using Quantized Information. IMA Journal of Mathematical Control and Information, vol. 34, no. 3, pp. 1061-1077, 2017. [PDF]
  4. Dong Shen, Yun Xu. Iterative Learning Control for Discrete-time Stochastic Systems with Quantized Information. IEEE/CAA Journal of Automatica Sinica, vol. 3, no. 1, pp. 59-67, 2016. [PDF]
Topic: ILC for Multi-source Systems
  1. Dong Shen, Chen Liu, Lanjing Wang, Xinghuo Yu. Iterative Learning Tracking for Multi-Sensor Systems: A Weighted Optimization Approach. IEEE Transactions on Cybernetics. DOI: [PDF][WebLink] TOP, REGULAR PAPER
Topic: ILC with Iteration Variable Gains
  1. Dong Shen, Jian-Xin Xu. A New Iterative Learning Control Algorithm with Gain Adaptation for Stochastic Systems. IEEE Transactions on Automatic Control. DOI: 10.1109/TAC.2019.2925495 [WebLink] TOP
Topic: ILC for Multi-Agent Systems
  1. Dong Shen, Chao Zhang, Jian-Xin Xu. Distributed Neural Networks Based Learning Consensus Control for Heterogeneous Nonlinear Multi-Agent Systems. International Journal of Robust and Nonlinear Control, vol. 29, no. 13, pp. 4328-4347, 2019. DOI: 10.1002/rnc.4627 [PDF][WebLink] TOP
  2. Chen Liu, Dong Shen, JinRong Wang. Iterative Learning Control of Multi-Agent Systems under Communication Noises and Measurement Range Limitations. International Journal of Systems Science, vol. 50, no. 7, pp. 1465-1482, 2019. [PDF][WebLink]
  3. Dong Shen, Jian-Xin Xu. Distributed Learning Consensus for Heterogenous High-Order Nonlinear Multi-Agent Systems with Output Constraints. Automatica, vol. 97, pp. 64-72, 2018. [PDF][WebLink] TOP
  4. Dong Shen, Jian-Xin Xu. Distributed Adaptive Iterative Learning Control for Nonlinear Multi-Agent Systems with State Constraints. International Journal of Adaptive Control and Signal Processing, vol. 31, no. 12, pp. 1779-1807, 2017. [PDF]
Topic: Point-to-Point ILC and Terminal ILC
  1. Jian Han, Dong Shen, Chiang-Ju Chien. Terminal Iterative Learning Control for Discrete-Time Nonlinear Systems Based on Neural Networks. Journal of the Franklin Institute, vol. 355, no. 8, pp. 3641-3658, 2018. [PDF]
  2. Yun Xu, Dong Shen, Xiao-Dong Zhang. Stochastic Point-to-Point Iterative Learning Control Based on Stochastic Approximation. Asian Journal of Control, vol. 19, no. 5, pp. 1748-1755, 2017. [PDF]
  3. Dong Shen, Jian Han, Youqing Wang. Stochastic Point-to-Point Iterative Learning Tracking Without Prior Information on System Matrices. IEEE Transactions on Automation Science and Engineering, vol. 14, no. 1, pp. 376-382, 2017. [PDF]
Topic: ILC for Stochastic Nonlinear Systems
  1. Dong Shen, Chao Zhang. Learning Control for Discrete-Time Nonlinear Systems With Sensor Saturation and Measurement Noise. International Journal of Systems Sciences, vol. 48, no. 13, pp. 2764-2778, 2017. [PDF]
  2. Dong Shen, Han-Fu Chen. A Kiefer-Wolfowitz Algorithm Based Iterative Learning Control for Hammerstein-Wiener Systems. Asian Journal of Control, vol. 14, no. 4, pp. 1070-1083, 2012. [PDF]
  3. Dong Shen, Han-Fu Chen. Iterative learning control for Large Scale Nonlinear Systems with Observation Noise. Automatica, vol. 48, no. 3, pp. 577-582, 2012. [PDF] TOP
  4. Dong Shen, Zhongsheng Hou. Iterative Learning Control with Unknown Control Direction: A Novel Approach. IEEE Transactions on Neural Networks, Special Issue on Data-based Optimization, Control and Modeling, vol. 22, no. 12, pp. 2237-2249, 2011. [PDF] TOP, REGULAR PAPER
  5. Dong Shen, Yutao Mu, Gang Xiong. Iterative Learning Control for Nonlinear Systems with Dead-zone Input and Time-delay in Presence of Measurement Noise. IET Control Theory and Applications, vol. 5, no. 12, pp. 1418-1425, 2011. [PDF]
  6. Dong Shen, Han-Fu Chen. Iterative Learning Control for a Class of Nonlinear Systems. Journal of System Science & Mathematics Sciences, vol. 28, no. 9, pp. 1053-1064, 2008 (In Chinese). [PDF]
Topic: ILC using Sampled-data
  1. Yun Xu, Dong Shen, Youqing Wang. On Interval Tracking Performance Evaluation and Practical Varying Sampling ILC. International Journal of Systems Science, vol. 48, no. 8, pp. 1624-1634, 2017. [PDF]