基于滚动时域的无人机空战决策专家系统

针对专家系统法在空战应用中存在适应性差的缺陷,提出了一种基于滚动时域控制(RHC)的机动决策算法对空战机动决策专家系统进行改进.首先,系统地分析了在专家系统空战机动决策中的最优控制问题,完成了机动决策最优控制模型系统状态方程的建立、控制约束的设计以及指标函数的建立.在此基础上,根据滚动时域法原理,将整个空战过程分解为若干有限时域,并在每个时域内将空战机动决策问题视为初始条件不断更新的专家系统机动决策最优控制模型的求解,反复进行直到空战结束.仿真结果表明,在专家系统法失效的情况下,通过求解专家系统空战机动决策滚动时域最优控制模型,无人机能够快速地进行有效的机动决策.

An UAV air-combat decision expert system based on receding horizon control

Aiming at the poor adaptability of expert system in air combat, a maneuvering decision algorithm based on the receding horizon control (RHC) method was proposed to improve the air combat maneuvering decision-making expert system. Firstly, the optimal control problem was systematically analyzed in the air combat maneuvering decision-making expert system. The system state equation, the index function and the control constraints of the maneuvering decision-making optimal control model were established. On this basis, according to the principle of the RHC method, the whole air combat process was divided into some sequential ones with the finite time horizon. In each time horizon, the optimal control model of the maneuvering decision-making expert system was solved to conduct air combat maneuvering decisions with initial state updated. The process was repeated until the air combat was over. The simulation result shows that, through solving the RHC optimal control model of the air combat maneuvering decision-making expert system, the unmanned aerial vehicle (UAV) can rapidly take effective maneuvering decisions in the case of expert system failure.