Fixed-Time Neural Control of a Quadrotor UAV With Input and Attitude Constraints
Benke Gao, Yan‐Jun Liu, Lei Liu
Abstract
This letter is concerned with the attitude control of a quadrotor unmanned aerial vehicle (UAV) subject to the input constraint, attitude constraint and model uncertainty. Firstly, we construct an auxiliary system to eliminate the adverse impact of the input saturation. Secondly, we introduce the nonlinear state-dependent function to deal with the attitude constraint directly. Thirdly, the neural network is utilized to identify the unknown terms in the system. Finally, with the help of the backstepping technology, a fixed-time control scheme is presented, which guarantees that the desired signal is followed with the fixed-time convergent rate. The effectiveness of the proposed scheme is validated by a simulation verification.