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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Main Contributions

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. 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, Jian-Xin Xu. Adaptive Learning Control for Nonlinear Systems with Randomly Varying Iteration Lengths. IEEE Transactions on Neural Networks and Learning Systems.

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.

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.

Topic 3: 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:

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.

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.

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 4: 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.

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, 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.

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. 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

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, Jian-Xin Xu. Adaptive Learning Control for Nonlinear Systems with Randomly Varying Iteration Lengths. IEEE Transactions on Neural Networks and Learning Systems.

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.

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.

Topic 3: ILC with Quantization

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:

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.

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.

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 4: ILC with Sampled Data

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.

Topic 5: ILC for Multi-agent Systems

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, 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

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

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.

- 61673045, Robustness of Iterative Learning Control under Incomplete Data and Control System Design, National Natural Science Foundation of China, 2017.01-2020.12
- 4152040, Design and Analysis of Iterative Learning Control under Random Packet Losses, Beijing Natural Science Foundation, 2015.01-2017.12
- 61304085, Design and Analysis of Iterative Learning Control Algorithms of Stochastic Systems for Unusual Tracking References, National Natural Science Foundation of China, 2014.01-2016.12
- G-JG-XJ201404, Mathematics Competency Cultivation for Graduate Student in Automation Discipline, Beijing University of Chemical Technology, 2015.01-2016.12
- Advanced Iterative Learning Control, the High-Level Talents Launching Funds, Beijing University of Chemical Technology, 2012.07-2015.06
- ZY1318, Stochastic
Iterative
Learning Control for Iteration-Varying Reference Trajectories,
Beijing University of Chemical Technology, 2013.01-2014.12