This paper proposes a new 5D chaotic system with the flux‐controlled memristor. The dynamics analysis of the new system can also demonstrate the hyperchaotic characteristics. The design and analysis of adaptive synchronization for the new memristor‐based chaotic system and its slave system are carried out. Furthermore, the modularized circuit designs method is used in the new chaotic system circuit implementation. The Multisim simulation and the physical experiments are conducted, compared, and matched with each other which can demonstrate the existence of the attractor for the new system.

Considering mismatched disturbances, aerodynamic interference, chattering, and actuator failure in the attitude control of the quadrotor unmanned aerial vehicle (UAV), this paper establishes a new quadrotor UAV model with mismatched disturbances, based on quaternion, and designs a fault tolerant controller. First, in order to reduce the chattering of the traditional reaching law, a new reaching law based on the sigmoid function is introduced into the design. Second, the sliding mode control and backstepping control methods are adopted, based on the new fractional-order sliding mode surface when the faults occur in quadrotor UAV actuators, and parameters in the sliding mode control are adaptively adjusted. The simulation results show that the fault tolerant control method can control the attitude of UAV quickly and achieve good robustness.

In order to comprehensively evaluate the risk of bird strike at airports and effectively prevent the occurrence of bird strike events, this paper constructs the risk assessment index system of airport bird strike from five perspectives of “personnel-bird-equipment-environment-management”. For the purpose of maximizing variances, the Analytic Hierarchy Process (AHP) and the entropy weight method are combined and used to obtain the comprehensive weights. The five-element connection number of Set Pair Analysis (SPA) is introduced to establish the identical-discrepancy-contrary airport bird strike risk assessment model, and the risk trend is analyzed according to the partial connection number for each order. The experiment results show that the combined weighting method can minimize the weight deviation and demonstrate good accuracy in determining the weights of indicators at all levels. The established airport bird strike risk assessment model can reasonably predict the risk trend, which is significant for airport personnel to carry out bird strike prevention works.

With the increased demands of airlines, it is important to study the location selection strategy for spare parts central warehouse in order to improve the allocation capacity of spare parts maintenance resources and reduce the operating costs of airlines. Based on the M/M/s/∞/∞ multiservice desk model and Multi‐Echelon Technique for Recoverable Item Control (METRIC) theory, this paper proposes a spare parts supply strategy based on the spare parts pool network and establishes a location selection model for spare parts central warehouse. The particle swarm optimization (PSO) algorithm is used to iteratively optimize the location for spare parts central warehouse and adjust the location area of the central warehouse combining transportation facilities and geographical environment factors. Finally, the paper compares the operating results for multiple airlines in pooling and off‐pooling states and verifies the effectiveness of the spare parts supply model and the advantages of cost control for airlines.

This paper proposes a U-model-based fault-tolerant controller design method in order to ensure the unmanned aerial vehicle (UAV) flight performance when subject to the actuator failures. Depending on the decoupled quadrotor model, this paper presents a sliding mode control method based on U-model in detail and realizes fault-tolerant control for the quadrotor UAV with the stability theory and simulation experiment verifications. The results show that the new controller designed by using the U-model method can simplify the controller design process which has good fault-tolerant characteristics when actuator faults occur compared with the traditional method.

To address the issues of low solving efficiency and susceptibility to local optima in multi-unmanned aerial vehicle (multi-UAV) task allocation algorithms within urban areas, this study constructs a task allocation model aiming to minimize economic costs for material delivery and reduce the urgency of rescue task demands. A multi-strategy clustering ant colony optimization algorithm (KMACO) is proposed for solution. Specifically, the K-means clustering method is utilized to partition the number of rescue tasks assigned to each UAV. In the ant colony optimization algorithm, a pheromone update strategy and a random evolution strategy are introduced to guide population search directions, thereby enhancing solving efficiency and avoiding local optima. Experimental results demonstrate that the proposed algorithm effectively reduces algorithm running time and operational economic costs while satisfying rescue task urgency requirements. Compared with conventional methods, KMACO shows superior performance in convergence speed and solution quality, thus providing an optimized decision-making framework for emergency rescue operations in complex urban environments.