Aiming at the demand of multi-UAV missions in urban low-altitude environment, combined with the concept of low-altitude economy, the urban environment is divided into multiple layers in the vertical direction to accomplish path planning. The traditional ant colony algorithm has problems such as slow planning speed and easy to fall into local optimization when performing UAV urban environment path planning. In order to improve these problems, we combine the Ant Colony Algorithm (ACO) with the Particle Swarm Algorithm (PSO), and utilize the early and fast convergence of the PSO to generate a suboptimal solution as the initial condition of the pheromone distribution of the ACO. Meanwhile, parameter adaptive optimization and forbidden strategy optimization were performed for the ACO, and parameter adaptive optimization and local optimum optimization were performed for the PSO. In addition, the energy consumption constraints and layering constraints of the UAVs are considered, and finally the paths are optimized using NURBS curves so that each UAV can reach the end point of its respective layer. The experimental results show that the average optimal fitness of the algorithm is improved by 22.2% and the algorithm running time is reduced by 33.0% compared to the traditional ACO.

Finite-time leader-following consensus problem of second-order multi-agent system (MAS) with nonlinear dynamics under directed communication topology is investigated in this paper. Based on the event triggered mechanism, a novel finite-time integral sliding mode control strategy is proposed to guarantee that the consensus stability of MAS can be reached within the upper limit of the time, which can be predicted according to the initial state of MAS. The event-triggered condition on the basis of the defined novel measurement error effectively reduces the energy dissipation of the system and the update frequency of the controller, and the deduced lower bound expression of the event-triggered time interval avoids the occurrence of zeno behavior. Numerical simulation results illustrate the effectiveness and feasibility of the proposed algorithm.

Toward the quadcopter unmanned aerial vehicle (UAV) attitude measurement problem, to improve the accuracy of acquisition of vehicle attitude parameters, and ensure accuracy of subsequent attitude control, the Quaternion-based Unscented Kalman filter (QUKF) data fusion method is presented. This attitude measurement system uses STM32F103 as the central controller, MPU6050 with integrated accelerometer and gyroscope, and magnetometer HMC5883l as the measurement sensor. Coordinate Rotation Relationships for the Attitude Heading Reference System (AHRS) in Quaternions, combining Unscented Kalman Filter (UKF) to Fuse Low-Cost Attitude Measurement Systems, tracking estimation of the genuine attitude of the vehicle. High-precision sensor measurements as real values, by comparing with Extended Kalman Filter (EKF), Complementary Filter (CF), and genuine values to validate and analyze the effectiveness of the algorithm applied to the low-cost attitude measurement system. Experimental results show that the low-cost attitude measurement system using quaternions as state variables combined with UKF can accurately estimate attitude information, providing precise attitude information for subsequent attitude control of UAVs.

A combined control method integrating Linear Active Disturbance Rejection Control (LADRC) and Sliding Mode Control (SMC) is proposed to mitigate model uncertainty and external disturbances in the attitude control of fixed−wing unmanned aerial vehicles (UAVs). First, the mathematical and dynamic models of a small fixed−wing UAV are constructed. Subsequently, a Linear Extended State Observer (LESO) is designed to accurately estimate the model uncertainties and unidentified external disturbances. The LESO is then integrated into the control side to enable the SMC to enhance the control system’s anti−interference performance due to its insensitivity to variations in−system parameters. The system’s stability is proven using the Lyapunov stability theory. Finally, simulations comparing the classical LADRC and the newly developed SMC−LADRC reveal that the latter exhibits strong robustness and anti−interference capabilities in scenarios involving model uncertainty, external disturbances, and internal disturbances, confirming the effectiveness of this control method.