Adaptive event-triggered quantized attitude control for QUAV with appointed-time prescribed performance function

This study proposes an adaptive event-triggered (ET) quantized attitude control for quadrotor unmanned aerial vehicle (QUAV) with an appointed-time prescribed performance function (PPF). The appointed-time PPF is developed to enforce attitude convergence to preset transient and steady regions within an appointed time. Furthermore, the radial basis function neural network (RBFNN) is adopted to tackle the system uncertainties. The disturbance observer is employed to estimate the compound disturbance of the optimal approximation error of RBFNN and the unknown external disturbance. Additionally, to reduce the communication burden of QUAV, the control signal derived from the backstepping method is transmitted to the actuator at every ET time after being quantified by the quantizer. A switching threshold strategy is proposed in the ET mechanism and a logarithmic uniform hysteresis quantizer is developed, better ensuring the system performance. Finally, the theoretical analysis proves that the signal of the closed-loop system is uniformly ultimately bounded without Zeno behavior. The superiority of the proposed attitude control strategy for QUAV is demonstrated through simulation results.