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.

In recent years, with the rapid development of swarm control technology, the swarm system has shown broad application prospects in military, civil, and other fields, which has gradually become a research hotspot in the area of automatic control. To further promote the development of swarm technology, this paper reviews the detail research on swarm control especially the results after 2018. In order to cover all the important and hot topics in the field of swarm control research, we conducted a literature search based on the Google Scholar and Web of Science databases and collected hundreds of articles on swarm control for review. The topics include swarm, formation, consistency, topology switching, delay control, convergence time control, obstacle avoidance, path planning, etc. Besides, the evaluation methods and application like vicinagearth security are introduced. Then, based on the latest existing research methods and results, the future research directions is proposed, including survival intelligence-based, learning-based, and heterogeneous control-based. In conclusion, the main purpose of this paper is to sort out the latest achievements in the development of swarm control technology, providing important research references for scholars.

The maneuvering of a large-scale unmanned aerial vehicle (UAV) swarm, notable for flexible flight with collision-free, is still challenging due to the significant number of UAVs and the compact configuration of the swarm. In light of this problem, a novel parallel control method that utilizes space and time transformation is proposed. First, the swarm is decomposed based on a grouping-hierarchical strategy, while the distinct flight roles are assigned to each UAV. Then, to achieve the desired configuration (DCF) in the real world, a bijection transformation is conducted in the space domain, converting an arbitrarily general configuration (GCF) into a standard configuration (SCF) in the virtual space. Further, to improve the flexibility of the swarm, the time scaling transformation is adopted in the time domain, which ensures the desired prescribed-time convergence of the swarm independent of initial conditions. Finally, simulation results demonstrate that collision-free maneuvering, including formation changes and turning, can be effectively and rapidly achieved by the proposed parallel control method. Overall, this research contributes a viable solution for enhancing cooperation among large-scale UAV swarms.

Based on cloud-fog computing, a cooperative control method is proposed for an air-ground heterogeneous swarm and the event-triggered strategy is introduced to eliminate the adverse effects of cyber attack. First, an air-ground nonlinear swarm system with aggregation-dispersion properties is built. Simultaneously, for better coordination and scalability of the heterogeneous swarm, a could-fog computing architecture dividing the swarm into two control layers is presented. As the swarm scales up, its susceptibility to random cyber-attacks increases, particularly to Denial of Service (DoS) attacks. Thus, the fog-cloud cooperative control method is proposed to ensure the secure consistency of the swarm under DoS attacks. In this way, a distributed average consensus control law at the fog layer is designed, while a leader-following-based asymptotic consensus control algorithm at the cloud layer is presented. Finally, numerical simulations are performed to verify the correctness and effectiveness of the proposed method, which has the strong robustness and scalability of the large-scale swarm system.

In this study, a hybrid air-sea swarm control methodology is introduced to achieve resilience against denial-of-service attacks, where such attacks intermittently block communication links to disrupt coordination. We start by establishing a swarm system comprising unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with distinct dynamic properties and degrees of connectivity. Unlike existing studies with homogeneous communication links, we consider heterogeneous (hybrid) UAV-to-UAV, USV-to-USV, and UAV-to-USV communication links. A cooperative-competitive interaction strategy is studied, where the swarm is divided into two clusters based on vehicle type. To face cyber vulnerabilities, an event-triggering security-oriented control law is designed, which is proven to counteract attacks while balancing communication and control resource usage. Furthermore, the relation between communication weights and system resilience to DoS attacks is analyzed. Numerical simulations demonstrate the efficacy and resilience of the proposed approach.

To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.