基于自适应动态规划的多对一追逃博弈策略

Many-to-One Pursuit-Evasion Game Strategy Based on Adaptive Dynamic Programming

针对多对一追逃博弈(PE)问题,提出了显性协同框架下的最优追逃控制策略。首先,利用图论工具将多对一追逃博弈问题转化为多智能体系统一致性控制问题;然后,结合自适应动态规划(ADP)技术,设计评价网络对追逃双方控制策略进行在线求解,并利用Lyapunov法证明稳定性。考虑到追逃策略总是成对出现,单个逃逸者面对多方追击时存在多个逃逸策略难以选择的问题,提出整体逃逸策略是各单一逃逸策略的动态加权的控制算法;最后,通过对导弹协同攻防过程建模并进行对比仿真,证明了所提博弈策略的有效性。

To the problem of many-to-one Pursuit-Evasion(PE) game strategy,an optimal PE control strategy is proposed in the framework of explicit cooperative guidance.Initially,based on graph theory,the many-to-one PE game problem is transformed into a multi-agent consensus control problem.Then,a critic neural network is utilized to solve the control strategies online by Adaptive Dynamic Programming(ADP) technology. The stability of the system is proved by Lyapunov direct method.Considering that the PE strategies always appear in pairs and it is difficult for a single evader to choose escape strategy,an overall escape strategy calculation method using dynamic weighting is proposed.Finally,a two-dimensional multi-missile attack and defense game model is established to ensure the effectiveness of the game strategy.