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. 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.