SHEN-Research

Topic 1: ILC with Random Data Dropouts

Summary:
In this topic, we have studied various data dropout models (e.g., stochastic sequence model, Bernoulli random variable model, and Markov chain model), controlled plants (e.g., linear model and nonlinear model), dropout positions (e.g., one-sided and two-sided dropouts), update laws (e.g., intermittent update scheme and successive update scheme), and convergence senses (e.g., expectation sense, mean square sense, and almost sure sense).
Representive publications:

Dong Shen. Iterative Learning Control with Incomplete Information: A Survey. IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 5, pp. 885-901, 2018.

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.

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.

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.

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.

Topic 2: ILC with Randomly Varying Lengths

Summary:
In this topic, we focus on the problem that the actual operation length varies in different iterations randomly. We have formulated the random iteration length by a random variable and established the strong convergence results using the probability theory. We have considered both discrete-time and continuous-time systems.
Representive publications:

Dong Shen, Xuefang Li. A Survey on Iterative Learning Control with Randomly Varying Trial Lengths: Model, Synthesis, and Convergence Analysis. Annual Reviews in Control, vol. 48, pp. 89-102, 2019.

Dong Shen, Samer S. Saab. Noisy Output Based Direct Learning Tracking Control with Markov Nonuniform Trial Lengths Using Adaptive Gains. IEEE Transactions on Automatic Control, vol. 67, no. 8, pp. 4123-4130, 2022.

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.

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.

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.

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.

Topic 3: ILC over Fading Channels

Summary:
In this topic, we have studied iterative learning control design and analysis topics in the presence of fading channels. Here, by fading we imply the multiplicable randomness associated with the signals while transmitting over wireless networks.
Representive publications:

Dong Shen. Practical Learning-Tracking Framework Under Unknown Nonrepetitive Channel Randomness. IEEE Transactions on Automatic Control, early access.

Shunhao Huang, Dong Shen, JinRong Wang. Point-to-Point Learning Tracking Control via Fading Communication Using Reference Update Strategy. IEEE Transactions on Cybernetics, early access.

Ganggui Qu, Dong Shen, Xinghuo Yu. Batch-Based Learning Consensus of Multi-Agent Systems With Faded Neighborhood Information. IEEE Transactions on Neural Networks and Learning Systems, early access.

Dong Shen, Xinghuo Yu. Learning Control over Unknown Fading Channels Based on Iterative Estimation. IEEE Transactions on Neural Networks and Learning Systems, early access.

Dong Shen, Ganggui Qu, Xinghuo Yu. Averaging Techniques for Balancing Learning and Tracking Abilities Over Fading Channels. IEEE Transactions on Automatic Control, vol. 66, no. 6, pp. 2636-2651, 2021.

Dong Shen, Xinghuo Yu. Learning Tracking Control Over Unknown Fading Channels Without System Information. IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 6, pp. 2721-2732, 2021.

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, vol. 31, no. 4, pp. 1196-1210, 2020.

Topic 4: ILC with Quantization

Summary:
In this topic, we consider the problem that the signal is first quantized and then transimitted, so that the transmission burden can be effectively reduced for practical applications. In particular, we have proposed an error-quantization method to ensure zero-error tracking performance for static logarithm quantizer. We have also introduced an encoding and decoding mechanism for the simple uniform quantizer with a strict zero-error tracking performance analysis.
Representive publications:

Dong Shen, Niu Huo, Samer S. Saab. A Probabilistically Quantized Learning Control Framework for Networked Linear Systems. IEEE Transactions on Neural Networks and Learning Systems.

Dong Shen, Chao Zhang. Zero-Error Tracking Control under Unified Quantized Iterative Learning Framework via Encoding-Decoding Method. IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 1979-1991, 2022.

Niu Huo, Dong Shen. Encoding-decoding Mechanism-based Finite-level Quantized Iterative Learning Control with Random Data Dropouts. IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, pp. 1343-1360, 2020.

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.

Topic 5: ILC for Multi-agent Systems

Summary:
In this topic, we have studied the learning consensus problem of multi-agent systems with output constraints. A general-type barrier function is introduced to solve the state/output constraints problem.
Representive publications:

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.

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.

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.

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.

Topic 6: Point-to-Point ILC and Terminal ILC

Summary:
In this topic, we consider the problem that the desired reference is a set of individual points/positions rather than a compelete trajectory. In this case, the input signal can be continuous, step-functions, and time-invariant. For point-to-point ILC problem, we have proposed an equivalent formulation of the problem and estibalished a stochastic approximation based framework. For terminal ILC problem, we have proposed an adaptive solution using neural networks.
Representive publications:

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.

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.

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.

Topic 7: ILC for Stochastic Nonlinear Systems

Summary:
In this topic, we have studied various stochastic nonlinear systems, such as affine nonlinear systems with hard-nonlinearities (deadzone, saturation, and preload), Hammerstein-Wiener Systems, and large-scale systems. The stochastic approximation based framework for solving these systems are established.
Representive publications:

Dong Shen, Han-Fu Chen. ILC for Large Scale Nonlinear Systems with Observation Noise. Automatica, vol. 48, no. 3, pp. 577-582, 2012.

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.

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.

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.

Topic 8: ILC with Sampled Data

Summary:
In this topic, we have established the upper bound estimation of interval tracking errors for sampled-data based ILC, which is the first time to give a sight beyond the at-sample performance in the existing literature.
Representive publications:

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.