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

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.在诸多应用领域中，直升机系统相较于固定翼飞机具备诸多显著优势。研发可提升此类系统跟踪精度的控制方案至关重要。本文针对两自由度直升机系统，提出了一种基于神经网络（Neural Network, NN）的自适应有限时间控制方案。具体而言，本文采用径向基函数神经网络（Radial Basis Function NN, RBFNN）处理直升机系统中的不确定性问题。此外，本文提出了一种带有切换阈值的事件触发机制，以减轻系统的通信负载。通过引入自适应参数、有界估计方法以及光滑函数策略，本文有效补偿了网络测量误差带来的影响，同时避免了芝诺现象（Zeno Phenomenon）的发生。此外，所提出的基于神经网络的自适应有限时间控制技术可确保跟踪误差实现有限时间收敛，进而提升系统的控制精度。借助李雅普诺夫直接法（Lyapunov Direct Method）可证明，闭环系统具备半全局有限时间稳定性。最后，仿真与实验结果验证了该控制策略的有效性。