Adaptive neural network-based trajectory tracking outer loop control for a quadrotor

This manuscript introduces a novel adaptive neural network-based controller for trajectory tracking of quadrotors. This controller is conceived as an outer loop controller that interacts with an inner loop controller in a two-loop configuration. The inner loop in this two-loop configuration is assumed to be inaccessible and unmodifiable, which is a realistic hypothesis in the operation of commercial quadrotors. Under this situation, the proposed controller computes appropriate kinematic input commands for the inner loop to achieve trajectory tracking. One remarkable feature of the proposed algorithm is its robustness against parametric uncertainties from the inner loop. An exhaustive error convergence analysis is provided, thus guaranteeing the convergence of the trajectory tracking error. Experimental results and a comparison using other control schemes demonstrate the competitiveness of the proposed scheme, being the latter the best among the tested adaptive neural network-based schemes.