Output-feedback asymptotic tracking control for rigid-body attitude via adaptive neural backstepping

Abstract

In this note, a novel neural adaptive output-feedback control (NAOC) with asymptotic tracking performance for rigid-body attitude is investigated subject to inertia uncertainty, unavailability of the angular velocity and unknown external disturbance. First, by virtue of the combination of the first-order filter and the coordinate transformation, the original output feedback system with immeasurable angular velocity and unknown dynamics is converted into the full-state strict feedback system with mismatched disturbance. Second, aided by infinitely integrable inequality with saturation function, an innovative neural network (NN) based adaptive control scheme is proposed via backstepping technique. By adopting the model transformation and proposed algorithm, the asymptotic tracking performance of the transformed system and the attitude tracking system without angular velocity can be achieved simultaneously. Finally, comparative numerical simulations illustrate the efficacy of the developed algorithm.