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Volume 10 Issue 8
Aug.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
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

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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  • 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.

     

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    Highlights

    • 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

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