Design and implementation of data-driven predictive cloud control system

doi:10.23919/JSEE.2022.000146

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (6): 1258-1268.doi: 10.23919/JSEE.2022.000146

• CONTROL THEORY AND APPLICATION • Previous Articles

Design and implementation of data-driven predictive cloud control system

Runze GAO(), Yuanqing XIA(), Li DAI(), Zhongqi SUN(), Yufeng ZHAN()

¹ School of Automation, Beijing Institute of Technology, Beijing 100081, China

Received:2022-03-29 Online:2022-12-18 Published:2022-12-24
Contact: Yuanqing XIA E-mail:gaorunze_bit@163.com;xia_yuanqing@bit.edu.cn;li.dai@bit.edu.cn;zhongqisun@bit.edu.cn;yu-feng.zhan@bit.edu.cn
About author:
GAO Runze was born in 1996. He received his B.S. degree in automation from Beijing Institute of Technology, China in 2017. He is currently working torward his Ph.D. degree in control science and engineering from Beijing Institute of Technology. His research interests include cloud control system, model predictive control and data-driven predictive control. E-mail: gaorunze_bit@163.com

XIA Yuanqing was born in 1971. He received his M.S. degree in fundamental mathematics from Anhui University, Hefei, China, in 1998, and Ph.D. degree in control theory and control engineering from Beijing University of Aeronautics and Astronautics, Beijing, China, in 2001. From 2002 to 2003, he was a post-doctoral research associate with the Institute of Systems Science, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing. From 2003 to 2004, he was with the National University of Singapore, Singapore, as a research fellow, where he researched on variable structure control. From 2004 to 2006, he was with the University of Glamorgan, Pontypridd, U.K., as a research fellow. From 2007 to 2008, he was a guest professor with Innsbruck Medical University, Innsbruck, Austria. Since 2004, he has been with the School of Automation, Beijing Institute of Technology, Beijing, first as an associate professor, then, since 2008, as a professor. His research interests include networked control systems, robust control and signal processing, and active disturbance rejection control. E-mail: xia_yuanqing@bit.edu.cn

DAI Li was born in 1988. She received her B.S. degree in information and computing science in 2010 and Ph.D. degree in control science and engineering in 2016, both from Beijing Institute of Technology, Beijing, China. Now she is an associate professor in the School of Automation, Beijing Institute of Technology. Her research interests include model predictive control, distributed control, data-driven control, stochastic systems, and networked control systems. E-mail: li.dai@bit.edu.cn

SUN Zhongqi was born in 1986. He received his B.E. degree in Computer and Automation in 2010 from Hebei Polytechnic University, Hebei, China, and the Ph.D. degree in Control Science and Engineering in 2018 from Beijing Institute of Technology, Beijing, China. During September 2018--August 2019, he was a postdoctoral fellow with the Faculty of Science and Engineering, University of Groningen, Netherlands. He is currently an assistant professor in the School of Automation of Beijing Institute of Technology. His research interests include multi-agent systems, model predictive control, machine learning, and robotic systems. E-mail: zhongqisun@bit.edu.cn

ZHAN Yufeng was born in 1989. He received his Ph.D. degree from Beijing Institute of Technology, Beijing, China, in 2018. He is currently an assistant professor in the School of Automation with Beijing Institute of Technology. Prior to join Beijing Institute of Technology, he was a post-doctoral fellow in the Department of Computing with the Hong Kong Polytechnic University. His research interests include networking systems, game theory, and machine learning. E-mail: yu-feng.zhan@bit.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61836001;62122014;62173036;62102022)

Abstract

Abstract:

The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems. Cloud computing is concerned as a powerful solution to handle complex large-scale control missions by using sufficient computing resources. However, the computing ability enables more complex devices and more data to be involved and most of the data have not been fully utilized. Meanwhile, it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms. Therefore, motivated by the above reasons, we propose a data-driven predictive cloud control system. To achieve the proposed system, a practical data-driven predictive cloud control testbed is established and together a cloud-edge communication scheme is developed. Finally, the simulations and experiments demonstrate the effectiveness of the proposed system.

Key words: cloud control system, data-driven predictive control, networked control system, cloud computing

Runze GAO, Yuanqing XIA, Li DAI, Zhongqi SUN, Yufeng ZHAN. Design and implementation of data-driven predictive cloud control system[J]. Journal of Systems Engineering and Electronics, 2022, 33(6): 1258-1268.

Figures/Tables 11

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References 19

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Time	Region	Max	Min	Mean
8:00–10:00	Beijing	0.0255	0.0055	0.0062
8:00–10:00	Guangzhou	0.0235	0.0185	0.0218
11:00–13:00	Beijing	0.0235	0.0075	0.0149
11:00–13:00	Guangzhou	0.0255	0.0190	0.0234
14:00–16:00	Beijing	0.0460	0.0025	0.0054
14:00–16:00	Guangzhou	0.0375	0.0185	0.0209
17:00–19:00	Beijing	0.0620	0.0025	0.0211
17:00–19:00	Guangzhou	0.0700	0.0175	0.0225

Symbol	Description	Parameter
$L$ /m	Length of the beam	0.4
$d$ /m	Radius of the gear	0.04
$R$ /m	Radius of the ball	0.015
$J_b$ /(kg·m²)	Moment of inertia of the ball	$9.9\times10^{-6}$
$m$ /kg	Mass of the ball	0.11
$g$ /(m/s²)	Acceleration of gravity	9.81
$F_N$	Support force on the ball	$-$

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Design and implementation of data-driven predictive cloud control system

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