Improved control system design of quadrotor helicopter based on integral neural sliding mode control

Regarding the characteristics of the current quadrotor helicopter control system i.e. strong coupling, complex nonlinear, multi-input multi-output and susceptibility to outside disturbance and modeling error, a double closed- loop control structure is proposed, involving attitude integral sliding mode robust control. The proposed controller consists of two parts: an angular velocity controller as the inner-loop and a position controller as the outer-loop. In which the switch function is realized by using integral sliding mode. The dynamic model of the quadrotor helicopter is established based on the Newton-Euler equation. The RBF neural network is used to adjust the gain of the sliding mode switching term and to adaptively learn the upper bound value of uncertain factors such as external disturbance and modeling error of system in order to weaken the chattering phenomenon caused by the conventional sliding mode control. The stability and exponential convergence of the closed-loop system have been proved according to Lyapunov theory, and the feasibility and effectiveness of the proposed approach is verified by numerical simulations.