多UAV协同搜索的博弈论模型及快速求解方法

在分布式模型预测控制（DMPC）方法框架下，提出了一种局部Nash最优的分布式搜索优化决策方法.设计了基于人工势场的协同机制，给出了势场的形成条件，在此基础上建立了多机协同的图论模型.引入局部Nash最优的定义，通过在连通分量的局部范围进行Nash最优迭代，降低了问题的求解维度.建立了以节点出度刻画UAV决策优先度的模型，根据决策偏序关系，提出了对称、主从、主从 对称3种决策形式，并给出了相应的Nash、Stackelberg和Nash Stackelberg的博弈模型，理论推导了该方法的计算复杂度.采用MPC和粒子群（PSO）优化相结合实现单架UAV的最优决策.仿真结果表明，所提出的方法能有效降低问题的求解规模和通信负担.

Game Theory Based Multi-UAV Cooperative Searching Model and Fast Solution Approach

This paper proposed a local Nash optimal based distributed search decision method in the frame of distributed model predictive control (DMPC). To consider the interaction between the UAVs, a graph theory based multi-UAVs cooperative model was constructed, which was based on artificial potential field (AFP) cooperative mechanism. It proposed a connected component based hierarchical structure that decomposes the complex optimization problem into smaller, more manageable sub-problems, to reduce the computational complex and communication burden. In this approach, a decision priority sequence is determined by node output degree. According to the decision priority, the paper proposed three decision forms: symmetry, leader-follower(LF) and symmetry-LF form. The corresponding game models were generated. The MPC and particle swarm optimization (PSO) based method was implemented to solve the individual UAV decision making. The simulations show that this is potentially a good method for solving cooperative search problem involving a large number of vehicles with robust performance.