摘要
针对多无人机(UAV)协同区域搜索问题展开研究。提出了一种基于分布式模型预测控制(DMPC)的多UAV分布式优化搜索方法。首先基于传统的搜索图模型,建立了多UAV协同搜索的问题描述和状态空间模型,然后在DMPC框架下,将集中式多UAV在线优化决策问题转化为各架UAV的小规模分布式优化问题,采用基于纳什最优和粒子群优化(PSO)相结合的算法实现对每个子系统优化问题的迭代求解。仿真结果表明:DMPC方法能够有效地降低多UAV协同搜索决策问题的求解规模,是一种可行的方法。
To deal with the problem of cooperative area search for multiple unmanned aerial vehicles（UAVs）,a decentralized optimization search method based on distributed model predictive control（DMPC）is presented in this article.First,based on the traditional search map model,a formal representation and system state space model of the multiple UAV cooperative search is established.Then,a centralized on-line optimization decision of the whole multiple UAV system is decomposed into the decentralized optimization of several single UAV subsystems under the framework of DMPC,and a Nash optimality and particle swarm optimization（PSO）based algorithm is implemented to the solution of the decentralized optimization.Simulation results demonstrate that the DMPC-based method can significantly reduce the size of multiple UAV optimization decision problems,and that it is a feasible approach for cooperative search.
出处
《航空学报》
EI
CAS
CSCD
北大核心
2010年第3期593-601,共9页
Acta Aeronautica et Astronautica Sinica
基金
国家"973"计划(6138101001)
国防科学技术大学优秀研究生创新基金(B080304)
关键词
无人机
协同区域搜索
分布式模型预测控制
粒子群优化
纳什最优
unmanned aerial vehicles
cooperative area search
distributed model predictive control
particle swarm optimization
Nash optimality
