Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators

Jianxiang Zhang; Baotong Cui; Xisheng Dai; Zhengxian Jiang

doi:10.1109/JAS.2019.1911663

Volume 7 Issue 3

Apr. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(3): 865-871

Jianxiang Zhang, Baotong Cui, Xisheng Dai and Zhengxian Jiang, "Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 865-871, May 2020. doi: 10.1109/JAS.2019.1911663

Citation:

Jianxiang Zhang, Baotong Cui, Xisheng Dai and Zhengxian Jiang, "Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 865-871, May 2020. doi: 10.1109/JAS.2019.1911663

Citation:

Jianxiang Zhang, Baotong Cui, Xisheng Dai and Zhengxian Jiang, "Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 865-871, May 2020. doi: 10.1109/JAS.2019.1911663

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Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators

doi: 10.1109/JAS.2019.1911663

Funds: This work was supported by National Natural Science Foundation of China (61807016) and Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX18-1859)

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Author Bio:
Jianxiang Zhang received the M.S. degree in control theory and control engineering from Guangxi University of Science and Technology. She is currently pursuing the Ph.D. degree with the Jiangnan University. Her research interests include iterative learning control, distributed parameter systems, and wireless sensor-actuator networks

Baotong Cui received the Ph.D. degree in control theory and control engineering from the College of Automation Science and Engineering, South China University of Technology in July 2003. He was a Post-Doctoral Fellow at Shanghai JiaoTong University from July 2003 to September 2005, and a Visiting Scholar at Department of Electrical and Computer Engineering, National University of Singapore, Singapore, from August 2007 to February 2008. He became an Associate Professor in December 1993 and a Full Professor in November 1995 at Department of Mathematics, Binzhou University,. He joined Jiangnan University in June 2003, where he is a Full Professor for the School of Internet of Things Engineering. His current research interests include systems analysis, artificial neural networks, and chaos synchronization

Xisheng Dai received the Ph.D. degree from South China University of Technology in 2010. He is currently a Professor at the School of Electrical and Information Engineering, Guangxi University of Science and Technology. His research interests include iterative learning control of distributed parameter systems and stochastic systems control

Zhengxian Jiang received the Ph.D. degree in control theory and control engineering from Jiangnan University in 2016. She is currently an Associate Professor at Jiangnan University. Her research interests include distributed parameter systems and wireless sensor-actuator networks
Corresponding author: J. X. Zhang and B. T. Cui are with the Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China (e-mail: zhangjianxiang155@163.com; btcui@jiangnan.edu.cn)
Received Date: 2018-11-28
Revised Date: 2019-02-28
Accepted Date: 2019-05-30

Available Online: 2019-07-13

Abstract

Abstract

In this paper, an open-loop PD-type iterative learning control (ILC) scheme is first proposed for two kinds of distributed parameter systems (DPSs) which are described by parabolic partial differential equations using non-collocated sensors and actuators. Then, a closed-loop PD-type ILC algorithm is extended to a class of distributed parameter systems with a non-collocated single sensor and m actuators when the initial states of the system exist some errors. Under some given assumptions, the convergence conditions of output errors for the systems can be obtained. Finally, one numerical example for a distributed parameter system with a single sensor and two actuators is presented to illustrate the effectiveness of the proposed ILC schemes.
- Actuators,
- distributed parameter system,
- iterative learning control,
- PD-type ILC scheme,
- sensors

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References(24)

References

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Highlights

We first propose the open-loop PD-type ILC scheme for a class of parabolic distributed parameter system with non-collocated sensors and actuators.
Then, we present a closed-loop PD-type ILC algorithm for the distributed parameter system using single sensor and multiple actuators when some errors are identified in the initial states of the system.
This study enhances the performance of parabolic distributed parameter system using non-collocated sensors and actuators.
The simulation results demonstrate the effectiveness of the proposed schemes.

Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators

doi: 10.1109/JAS.2019.1911663

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通讯作者: 陈斌, bchen63@163.com

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