Neural Network-based Robust Adaptive Certainty Equivalent Controller for Quadrotor UAV with Unknown Disturbances

In this paper, a robust adaptive neural network certainty equivalent controller for a quadrotor unmanned aerial vehicle is proposed, which is applied in the outer loop for position control to directly generate the desired roll and pitch angles commands and then to the inner loop for attitude control. The newly proposed controller takes into account the vehicle’s kinematic and modelling error uncertainties which are associated with external disturbances, inertia, mass, and nonlinear aerodynamic forces and moments. The control method integrates an adaptive radial basis function neural networks to approximate the unknown nonlinear dynamics with certainty equivalent control technique, in this way leading to the fact that precise dynamic model and prior information of disturbances are not needed. The adaptation law was derived by using a Lyapunov theory to verify the stability and superiority of the new algorithms. The performance and effectiveness are also verified by carrying out several simulations. It was shown from the analysis that the altitude, position, and attitude tracking errors are converged to zero and the closed loop stability is guaranteed under extreme conditions.