Neural adaptive attitude tracking control for uncertain spacecraft with preassigned performance guarantees

In this article, two novel neural adaptive attitude control strategies are presented for the attitude tracking of rigid spacecraft affected by inertia uncertainty, external disturbance, and torque saturation with guaranteed prescribed performance. Under the backstepping framework, a neural adaptive state feedback controller is designed first by utilizing the neural network (NN) approximation and barrier Lyapunov function (BLF). Then, on the basis of the state feedback controller, a neural adaptive output feedback controller is exploited by integrating with a high-gain observer. Lyapunov stability analysis shows that all the closed-loop error signals are uniformly ultimately bounded under the both proposed neural adaptive attitude controllers. In comparisons with the most existing researches, the distinctive advantages of the proposed neural adaptive attitude controllers are threefold. (1) With the help of NN approximation, the proposed attitude controllers are model-independent and still applicable even when the spacecraft attitude dynamics are completely unknown. (2) Benefiting from the BLF, the proposed attitude controllers can guarantee the attitude tracking error always satisfies the preassigned performance. (3) Both the state feedback control and output feedback control are considered. The angular velocity information of the spacecraft is not required for the output feedback control design. At last, numerical simulations and comparisons are conducted to verify (c) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.