Adaptive Neural Network Control of a Quadrotor with Input Delay

In this paper, an adaptive flight control scheme based on Radial Basis Function (RBF) neural network and state predictor is presented for a quadrotor with input delay and unknown disturbance. State predictor is designed to estimate the system state with time delays, and the RBF neural network is applied to approximate the uncertainties including modeling error, time-varying disturbance and predictive error. It is proven that the state errors can converge into the given neighborhood of the origin, and all the signals in the closed-loop system is proven to be uniform ultimate boundedness (UUB). Simulation results verify the effectiveness of the designed controller.