﻿ 基于滚动时域的无人机空战决策专家系统
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1. 沈阳航空航天大学航空航天工程学部, 沈阳 110136;
2. 沈阳航空航天大学自动化学院, 沈阳 110136;
3. 大连理工大学电子信息与电气工程学部, 大连 116024

An UAV air-combat decision expert system based on receding horizon control
FU Li1, XIE Fuhuai2, MENG Guanglei2, WANG Dongzheng3
1. Faculty of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China;
2. School of Automation, Shenyang Aerospace University, Shenyang 110136, China;
3. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
Abstract: 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.
Key words: receding horizon control(RHC)     optimal control model     maneuvering decision     expert system     air combat

1 专家系统机动决策最优控制模型的构建 1.1 空战机动决策专家系统基本结构

1.2 最优控制模型的构建

1.2.1 系统状态方程描述

1.2.2 控制约束设计

1.2.3 空战机动决策指标函数的建立

 α1,α2—目标方位角和目标进入角; vr,vb—我机速度矢量和敌机速度矢量; B—敌机位置;R(O)—我机位置;D—目标距离; DRmax—我机雷达最大探测距离; DMmax—我机导弹最大不可逃逸距离. 图 1 双机空战态势关系 Fig. 1 Relation of situation of two sides in air combat

1) 角度优势函数.

2) 距离优势函数.

3) 能量优势函数.

2 基于滚动时域控制的专家系统决策控制方法与仿真验证 2.1 基于滚动时域控制的专家系统控制方法

 图 2 滚动时域控制原理图 Fig. 2 RHC schematic

2.2 仿真验证

 战机 x/m y/m z/m v/(m·s-1) θ/(°) ψ/(°) 我机 10000 10000 10000 150 0 60 敌机 10000 0 10000 150 0 0

 图 3 原专家系统机动决策 Fig. 3 Expert system maneuver decision

 图 4 改进专家系统机动决策 Fig. 4 Improved expert system maneuver decision

3 结 论

1) 系统能充分发挥专家的经验优势,决策过程不需复杂的算法计算,有利于提高机动决策的实时性.

2) 系统具有良好的可维护性和扩展性.对于不同的机型,系统可以根据需要对知识库中的知识进行增删、修改、扩充等操作.

3) 系统具有更强的灵活性和适应性.系统综合使用专家系统法和滚动时域法进行决策,在任何的空战态势下,都能做出有效的机动决策.

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#### 文章信息

FU Li, XIE Fuhuai, MENG Guanglei, WANG Dongzheng

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

Journal of Beijing University of Aeronautics and Astronsutics, 2015, 41(11): 1994-1999.
http://dx.doi.org/10.13700/j.bh.1001-5965.2014.0726