Antisaturation fixed-time attitude tracking control based low-computation learning for uncertain quadrotor UAVs with external disturbances

External disturbances, uncertain parameters, asymmetric saturation input, and high computational burden can significantly damage the attitude tracking control performance of uncertain quadrotor unmanned aerial vehicles (UAVs). To accomplish the high-precision attitude tracking control, this study proposes an antisaturation fixed-time attitude tracking control based low-computation learning. Firstly, a fixed-time state observer is constructed to estimate the system states in fixed time. Secondly, by developing the fast fixed-time stable system with the time-varying gain function, a nonsingular fast fixed-time sliding mode surface is designed to improve the convergence speed and avoid the singularity problem. Thirdly, to solve the problem of asymmetric input saturation, an auxiliary compensation system is integrated to regulate the control inputs. Subsequently, an adaptive neural network (NN) technology is used to overcome the negative effects of external disturbances and uncertain parameters, where the designed adaptive mechanism is to adjust a virtual parameter online instead of the weight vector of the NN, which has the characteristics of low computational burden and simple structure. The Lyapunov-based stability analysis concludes that the closed-loop system is practical fixed-time stable and the tracking errors can converge to bounded regions around the origin in fixed time independently of the initial system states. Finally, comparative results are given to demonstrate that compared with the existing controllers, the controller developed in this study can achieve stronger robustness, faster convergence, and saturation elimination with lower error-index values.