| In recent years,with the advancement of aviation technology,multi-UAV coordinated formations have been widely used in both military and civilian fields.Aiming at the problems of high communication resource occupancy rate and changes in communication topology in the current research on UAV formation control algorithms,thesis conducts research from three aspects: UAV formation control,information interaction strategy,and self-organizing formation.The research content and results are as follows:Firstly,the formation and maintenance of multiple UAV formations is studied.Simplify the complex UAV formation control system into three-loop control systems of speed,heading angle,and altitude.Based on the consistency theory,design the consistency control algorithms of the three control systems separately,and combine the constraints of formation and expected state,a consensus-based multi-UAV cooperative formation control algorithm is proposed.The simulation results show that the algorithm can not only form and maintain high-precision and stable UAV formations,but also adapt to UAV formations of different sizes,and at the same time has the ability to change formations.Secondly,the problem of high occupancy of communication resources by UAV formations is studied.A dynamic event-triggered communication strategy algorithm with state prediction is proposed to manage the state information exchange between UAVs.The algorithm uses an event-triggered communication strategy instead of a periodic communication strategy to exchange status information between UAVs,which can effectively reduce the frequency of status information exchange,decrease the occupation of communication resources,and provide additional communication redundancy for other communication services.In addition,in order to reduce the impact of the reduction of state information on formation control,a state prediction algorithm based on the gray prediction model GM(1,1)is proposed to predict the state information of neighboring UAVs and to reduce the uncertainty of UAV formation control system,so as to improve the accuracy and stability of UAV formation.Compared with other algorithms,the algorithm in this thesis can not only effectively reduce the frequency of state information exchange between UAVs and improve the utilization efficiency of communication resources,but also can form and maintain a formation with higher precision and stability.Finally,the problem of frequent changes of UAV formation communication topology is studied.A kmeans-based UAV formation clustering algorithm is proposed to solve the communication topology changes caused by large-scale,high-mobility UAV formations,and at the same time,the UAV formation has the ability of self-organization,which can dynamically manage the formation shape and network.The algorithm is based on the idea of kmeans clustering,combined with the distribution of the initial UAV energy to improve the distance calculation method in kmeans,and uses the energy of the UAV in the cluster and the average distance to the neighbor UAVs to design the fitness function of the UAV.Finally,the cluster head is selected according to the maximum fitness value to establish a cluster.Simulation results show that the kmeans-based UAV formation clustering algorithm proposed in thesis can not only adapt to large-scale,high-speed moving UAV formations with frequent changes in communication topology but also enable UAV formations to have self-organization capabilities.Improve the overall performance of the UAV formation network and enhance the reliability and scalability of the formation. |
