2026-10-13T11:05:51Z https://www.scidb.cn/oai

10.57760/sciencedb.07287 2023-02-09T09:13:18Z

2023-02-09 Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System with an Event-Triggering Mechanism doi:10.57760/sciencedb.07287 en Helicopter systems present numerous benefits over fixed-wing aircraft in several fields of application. Developingcontrol 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 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-timeconvergence 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; eventtriggering mechanism; two-degree-of-freedom helicopter; finite time. Zhijia Zhao PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1109/jas.2023.123453 Science Data Bank