In this article, a distributed control algorithm for a large-scale fixed-wing unmanned aerial vehicle (UAV) swarm system of collision avoidance and formation maneuver is proposed, which can meet the demands of a dense swarm with collaborative autonomy. First, a novel structure of a swarm is introduced, which can provide the reserved maneuverable space for UAVs during the flight. Besides, inspired by the movement of bird flocks, the light transmission model is proposed to improve the conventional potential field functions, which can realize the UAV swarm with the capability of repulsion in the short-range, speed and position cooperation in the middle range, and attraction in the remote range. Then, the distributed control architecture and the control algorithm of UAVs are designed, and the stability of the swarm is proved correspondingly. In the end, several numerical simulation results show the effectiveness and superiority of the proposed method, which illustrates the distributed control architecture of the swarm system to be more adaptable, stable, and scalable. Therefore, the proposed control algorithm provides strong theoretical support for the actual application in a large-scale fixed-wing UAV swarm system collaborative autonomy.

This paper proposes a novel prescribed-time cooperative guidance law (PTCGL) for multiple missiles cooperatively attacking a manoeuvring target. Guidance models in two directions are used and the acceleration saturations are also considered in both two directions. In line-of-sight (LOS) direction, by applying prescribed-time multi-agent consensus theory, the problem of the times-to-go ( $T_{gos}$) of multiple missiles converging to a same value is transformed into a stabilisation problem. Then the cooperative guidance law in LOS direction is designed based on the prescribed time integral sliding mode surface (PTISMS) and a new variable coefficient exponential reaching law, and a first-order auxiliary system is introduced to deal with the input saturation problem; based on a prescribed time linear sliding surface (PTLSMS), the cooperative guidance law in normal LOS direction is designed to ensure the LOS angle and LOS angle rate convergence within a prescribed time, and a second-order auxiliary system is designed to deal with the input saturation problem. Furthermore, the prescribed time convergence of the PTCGL is proved with the Lyapunov stability theory. Finally, numerical simulations demonstrate the effectiveness and advantages of the proposed PTCGL.

The Internet of Things (IoT) system and path planning algorithm provide a technological foundation for autonomous navigation of uncrewed aerial vehicles (UAVs). Geospatial data from the IoT system is transmitted to UAVs through lightweight protocols, and UAVs make optimal path decisions based on these data through optimization algorithms. The combination of the IoT, UAV, and path planning technology constitutes a UAV delivery system, which offers an efficient and economical solution for last-mile logistics in smart cities. Among these, rapid and accurate optimal path planning is crucial for the autonomous delivery of UAVs. Therefore, this article proposes a multistrategy improved particle swarm optimization (PSO) algorithm called MSIPSO. First, the algorithm incorporates a local deadlock jump strategy to increase the success rate of path planning in dense obstacle environments. Second, to mitigate the influence of parameter selection on the algorithm’s performance, adaptive nonlinear inertia weights and learning factors are introduced to improve the algorithm’s stability. Finally, multiple population differentiation evolution strategies are designed, with different position update equations tailored for populations of varying qualities, which enhances the search efficiency of the algorithm. The simulation results show that MSIPSO outperforms PSO, gray wolf optimizer (GWO), whale optimizer (WOA), elite archive-driven PSO (EAPSO) algorithm, and hybrid GWO and differential evolution (HGWODE) algorithm in terms of convergence speed, accuracy, and stability.

Because of the limitation of the long-range detection capability of airborne sensors, it is very difficult for a single interceptor to achieve accurate mid-course guidance, especially for the long and medium range interceptors, and the maneuvering or invisible targets. As one of the solutions, the strategy of multi-interceptor cooperative intercepting can overcome the shortages of single-interceptor. Unfortunately, the existing mid-course guidance laws can't meet the requirements of cooperative intercepting strategy. To solve the problem, a novel cooperative mid-course guidance law is proposed in this paper by combining the traditional trajectory shaping guidance law and the cooperative parameter. Firstly, the cooperative mid-course guidance law is designed, and consensus of cooperative parameter characteristics is proved then. Secondly, the general expression of cooperative parameter and its convergence speed is analyzed. Finally, numeral simulations demonstrate the effectiveness and advantages of the proposed cooperative mid-course guidance law.

To address the issue that multiple missiles intercepting a highly maneuvering target simultaneously with desired angles, a novel cooperative guidance law based on the decoupled model in the line-of-sight (LOS) and normal LOS direction is proposed in LOS frame. However, there is control coupling in the two directions of the cooperative guidance model. In this article, we first decouple the guidance model and then separately design estimation decouple cooperative guidance law in two directions based on the decoupled model. The guidance law component in the LOS direction is designed with the integral sliding mode control and multi-agent consensus theory, where the combining disturbance is compensated by an adaptively estimated term. The guidance law component in the normal LOS direction is designed based on nonsingular terminal sliding mode control with fast power reaching law, and a nonlinear disturbance observer is introduced to estimate the combining disturbance. Then, the finite time convergence of time-to-go and LOS angle is proved. Finally, numerical simulation results demonstrate the effectiveness and superiority of the proposed cooperative guidance law.