Fixed-time neuro-sliding mode controller design for quadrotor UAV transporting a suspended payload

This study develops a fixed-time neuro-sliding mode controller to enhance the transient response and robustness of a quadrotor that is subjected to external disturbances and parameter uncertainties along with the dynamics of a suspended payload. A simple neural network with a fixed-time adaptive law is used to estimate unknown dynamics; therefore, no previous knowledge of dynamics is required to develop controllers. The neural network component will also help to eliminate the chattering effect of the sliding mode controller through its learning structure. Fixed-time convergence to desired trajectories is proven by detailed theoretical analysis based on the Lyapunov stability theorem, and to show the proposed controller's effective performance, thorough numerical simulations are run.