This study presents an observer-based adaptive fuzzy finite-time attitude control strategy for quadrotor unmanned aerial vehicles (UAVs). To estimate the information of angular velocity with the finite-time property, an adaptive neural network observer is first developed. Subsequently, an adaptive fuzzy logic system (FLS)-based nonsingular fast terminal sliding mode controller is proposed to compensate for the lumped disturbance and adjust the control gain online. To cope with the input saturation, an auxiliary system without the boundedness of the saturation difference is constructed. The theoretical analysis proves that all the system signals are bounded and the tracking errors can converge to small neighbourhoods in finite time. Finally, comparative simulations and experiments are performed to manifest the feasibility and superiority of the proposed control strategy, in terms of strong robustness, singularity avoidance, free-chattering, fault tolerance, and saturation attenuation.

In intelligent driving, situational assessment (SA) is an important technology, which helps to improve the cognitive ability of intelligent vehicles in the environment. Uncertainty analysis is very significant in situation assessment. This article proposes an SA method based on uncertainty risk analysis. Under uncertain conditions, according to the random environment model and Gaussian distribution model, the collision probability between multiple vehicles is estimated by comprehensive trajectory prediction. The proposed method considers collision probabilities of different prediction points within and outside the prediction range and obtains long-term accurate prediction results. The method is suitable for the situation risk assessment of sensor systems in the presence of unexpected dynamic obstacles, sensor failures or communication losses in traffic, and different environmental sensing accuracy. The experimental results show that in the dynamic traffic environment, the proposed scenario assessment method can not only accurately predict and assess the situation risks within the prediction range, but also provide accurate scenario risk assessment outside the prediction range.

This brief investigates the tracking control problem of the quadrotor unmanned aerial vehicle considering the disturbance and input saturation. According to the hierarchical control principle, the quadrotor dynamics is decomposed into position and attitude loops to accommodate the controller design. To overcome the disturbance, two disturbance observer-based state estimators are designed in two loops, respectively. By integrating the filtering-error compensation mechanism and auxiliary control system into the dynamic surface control, the input saturation and filtering errors can be solved. In addition, the tracking errors of the closed-loop system can converge to the origin. Finally, simulation results show the validity of the proposed control formulation.

This brief proposes an attitude tracking control method for a quadrotor subject to wind disturbance and input saturation. First, a finite-time extended state observer is designed to eliminate the wind disturbance and estimate the angular velocity. To achieve the fast finite-time convergence, singularity avoidance and chattering elimination, a fast nonsingular terminal sliding mode controller is developed. Next, an auxiliary system is constructed elaborately to address the input saturation. With the aid of Lyapunov technique, the stability analysis is provided to manifest that the tracking error converges to an arbitrarily small bounded region in finite time. Finally, simulation results demonstrate that the viability of the proposed control strategy.