Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

Zhijia Zhao; Jian Zhang; Shouyan Chen; Wei He; Keum-Shik Hong

doi:10.1109/JAS.2023.123453

Volume 10 Issue 8

Aug. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(8): 1754-1765

Z. J. Zhao, J. Zhang, S. Y. Chen, W. He, and K.-S. Hong, “Neural-network-based adaptive finite-time control for a two-degree-of-freedom helicopter system with an event-triggering mechanism,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1754–1765, Aug. 2023. doi: 10.1109/JAS.2023.123453

Citation:

Z. J. Zhao, J. Zhang, S. Y. Chen, W. He, and K.-S. Hong, “Neural-network-based adaptive finite-time control for a two-degree-of-freedom helicopter system with an event-triggering mechanism,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1754–1765, Aug. 2023. doi: 10.1109/JAS.2023.123453

Citation:

Z. J. Zhao, J. Zhang, S. Y. Chen, W. He, and K.-S. Hong, “Neural-network-based adaptive finite-time control for a two-degree-of-freedom helicopter system with an event-triggering mechanism,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1754–1765, Aug. 2023. doi: 10.1109/JAS.2023.123453

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Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

doi: 10.1109/JAS.2023.123453

Funds: This work was supported in part by the National Natural Science Foundation of China (62273112, 62061160371, 61933001, 51905115), the Science and Technology Planning Project of Guangzhou City (202201010758), the Guangzhou University-Hong Kong University of Science and Technology Joint Research Collaboration Fund (YH202205), the Open Research Fund from the Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen (SZ)) (GML-KF-22-27), and the Korea Institute of Energy Technology Evaluation and Planning Through the Auspices of the Ministry of Trade, Industry and Energy, Republic of Korea (20213030020160)

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Author Bio:
Zhijia Zhao (Member, IEEE) received the B.Eng. degree in automatic control from North China University of Water Resources and Electric Power in 2010, and the M.Eng. and Ph.D. degrees in automatic control from South China University of Technology, in 2013 and 2017, respectively.He is currently an Associate Professor at the School of Mechanical and Electrical Engineering, Guangzhou University. His research interests include adaptive and learning control, flexible mechanical systems, and robotics

Jian Zhang received the B.Eng. degree from West Anhui University in 2020. He is currently pursuing the master degree at the School of Mechanical and Electrical Engineering, Guangzhou University.His research interests include adaptive control, intelligent control, and robotics

Shouyan Chen received the B.Eng. degree in process control and the Ph.D. degree in mechanical design from the South China University of Technology, in 2008 and 2017, respectively.He is currently an Associate Professor at the School of Mechanical and Electrical Engineering, Guangzhou University. His research interests include robotics, human-robot interaction, and intelligent control

Wei He (Senior Member, IEEE) received the B.Eng. and the M.Eng. degrees from the College of Automation Science and Engineering, South China University of Technology, in 2006 and 2008, respectively, and the Ph.D. degree from the Department of Electrical and Computer Engineering, the National University of Singapore, Singapore, in 2011. He is now working as a Full Professor at the School of Intelligence Science and Technology, University of Science and Technology Beijing. He has co-authored 3 books published in Springer and published over 100 international journal and conference papers. He was awarded a Newton Advanced Fellowship from the Royal Society, UK in 2017. He was a recipient of the IEEE SMC Society Andrew P. Sage Best Transactions Paper Award in 2017. He is serving the Chair of IEEE SMC Society Beijing Capital Region Chapter. From 2018, he has been the Chair of Technical Committee on Autonomous Bionic Robotic Aircraft, IEEE Systems, Man and Cybernetics Society. He has been serving as an Associate Editor/Guest Editor of several prestigious scientific journals, including IEEE Transactions on Robotics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man, and Cybernetics: Systems, SCIENCE CHINA Information Sciences, IEEE/CAA Journal of Automatica Sinica, Neurocomputing, and an Editor of Journal of Intelligent and Robotic Systems. His research interests extend to robotics, distributed parameter systems, and intelligent control systems

Keum-Shik Hong (Fellow, IEEE) received the B.S. degree in mechanical design from Seoul National University, Korea (south) in 1979, the M.S. degree in mechanical engineering from Columbia University, USA, in 1987, and both the M.S. degree in applied mathematics and the Ph.D. degree in mechanical engineering from the University of Illinois at Urbana-Champaign, USA in 1991. He joined the School of Mechanical Engineering, Pusan National University, Korea (south) in 1993.He served as Associate Editor of Automatica (2000–2006), as Editor-in-Chief of the Journal of Mechanical Science and Technology (2008–2011), and is serving as the Editor-in-Chief of the International Journal of Control, Automation, and Systems. He was a past President of the Institute of Control, Robotics and Systems (ICROS), Korea, and is President of the Asian Control Association. He is an IEEE Fellow, a Fellow of the Korean Academy of Science and Technology, an ICROS Fellow, and a Member of the National Academy of Engineering of Korea. He has received many awards, including the Best Paper Award from the KFSTS of Korea (1999) and the Presidential Award of Korea (2007). His research interests include brain-computer interface, nonlinear systems theory, adaptive control, and distributed parameter systems
Corresponding author: Zhijia Zhao, e-mail: zhjzhaoscut@163.com; Shouyan Chen, e-mail: maxcsy@gzhu.edu.cn
Received Date: 2022-12-29
Accepted Date: 2023-01-26

Available Online: 2023-04-18

Abstract

Abstract

Helicopter systems present numerous benefits over fixed-wing aircraft in several fields of application. Developing control schemes for improving the tracking accuracy of such systems is crucial. This paper proposes a neural-network (NN)-based adaptive finite-time control for a two-degree-of-freedom helicopter system. In particular, a radial basis function NN is adopted to solve uncertainty in the helicopter system. Furthermore, an event-triggering mechanism (ETM) with a switching threshold is proposed to alleviate the communication burden on the system. By proposing an adaptive parameter, a bounded estimation, and a smooth function approach, the effect of network measurement errors is effectively compensated for while simultaneously avoiding the Zeno phenomenon. Additionally, the developed adaptive finite-time control technique based on an NN guarantees finite-time convergence of the tracking error, thus enhancing the control accuracy of the system. In addition, the Lyapunov direct method demonstrates that the closed-loop system is semiglobally finite-time stable. Finally, simulation and experimental results show the effectiveness of the control strategy.
- Adaptive neural-network control,
- event-triggering mechanism (ETM),
- finite time,
- two-degree-of-freedom helicopter

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References(38)

References

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A new event triggering mechanism (ETM) for greater flexibility and save communication resources
Utilize ETM to save system communication resources while considering finite time convergence
It ensures that the closed-loop signal of the system is half-leaf finite time stable

Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

doi: 10.1109/JAS.2023.123453

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通讯作者: 陈斌, bchen63@163.com

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