Research and Verification of Trajectory Tracking Control of a Quadrotor Carrying a Load

Abstract

This paper assumes that considering the unknown and time-varying nature of different strong and weak wind field disturbances and considering the nonlinear, under-driven, strongly coupled quadrotor carrying, a load is disturbed by the complex and variable wind field and unmodeled parts when flying in the real external environment, which will reduce the control effect of the nonlinear controller and make the vehicle fail to affect the flight effect. In order to ensure that the quadrotor carrying a load can carry supplies in the harsh environment for stable trajectory tracking, a neural network adaptive control algorithm is introduced in the article. The neural network algorithm has the role of online dynamic approximation, the compensation of arbitrary external disturbance and the compensation of external disturbance. Its structure is simple and low computation. In the article, the Lyapunov method is used to design the adaptive weight and estimate the weight of the online neural network, and the stability of the system is proved. Finally, the comparison of three algorithms verified by simulation proves that the above interference problem can be solved effectively by the proposed algorithm.