An adaptive anti-swing control for the helicopter slung-load system based on trajectory planning and neural network

摘要

In this article, an adaptive anti-swing control method based on trajectory planning and radial basis function neural network (RBFNN) is proposed for the unmanned helicopter slung-load system with the external disturbances and the continuous system uncertainty. An online trajectory planning method is designed to obtain the desired position, velocity, and acceleration signals of the unmanned helicopter. Then, combining with RBFNN and sliding mode control method, the position loop controller of the unmanned helicopter is designed to make the swing angle stable, and the unmanned helicopter can also track the desired trajectory. The attitude loop controller is designed by sliding mode backstepping method and RBFNN. In the process of controller design, the adaptive laws are designed to adjust the influence of approximation error of RBFNN and the external disturbance. The stability of the closed-loop system is proved by Lyapunov analysis. Numerical simulation results show that the proposed control strategy is effective.