多平台分布式协同作战下基于MPC-MAS的指挥控制模型设计

doi:10.3969/j.issn.1001-506X.2020.07.21

Abstract

Abstract:

In the rapidly changing networked system combat environment, it has always been one of the research hotspots about how to accurately and timely extract the useful information and make decisions to effectively deal with the problems of low combat effectiveness, large resource consumption, and long combat time in actual combat. Based on the background of multi-platform collaboration to complete the interception of incoming targets, a command control system model is proposed with the model prediction control (MPC) and multi-agent system (MAS) under multi-platform distributed collaborative operation. The model concept of multi-platform distributed cooperative enables the complete information sharing among various platforms, and adopts a global distribution-local centralized decision structure. A collaborative model framework for distributed command and control is designed. The simulation experiment results are selected for comparison with the Monte Carlo method, which prove that the model exhibits better convergence, low error, low loss value, and more effectively to solve the problem of constructing a command control model for collaborative operations in a distributed environment.

Key words: distributed cooperative operation, multi-agent, command control model, conflict resolution

CLC Number:

TP301.6

Jiayi LIU, Shaohua YUE, Gang WANG, Jie ZHANG, Xiaoqiang YAO. Design of command control model based on MPC-MAS under multi-platform distributed cooperative operation[J]. Systems Engineering and Electronics, 2020, 42(7): 1582-1589.

Figures/Tables 10

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References 22

1	谢光强, 章云. 多智能体系统协调控制一致性问题研究综述[J]. 计算机应用研究, 2011, 28 (6): 2035- 2039.
	XIE G Q , ZHANG Y . Synthesis of multi-agent system coordination and control consistency[J]. Application Research of Computers, 2011, 28 (6): 2035- 2039.
2	HE Y J, WANG W, WU X Z, et al. An overview of applications of MAS in smart distribution network with DG[C]//Proc.of the IEEE 2nd International Future Energy Electronics Conference, 2015.
3	CARIDI M , CAVALIERI S . Multi-agent systems in production planning and control: an overview[J]. Production Planning & Control, 2004, 15 (2): 106- 118.
4	MCARTHUR S D J , DAVIDSON E M , CATTERSON V M , et al. Multi-agent systems for power engineering applications——part Ⅱ: technologies, standards, and tools for building multi-agent systems[J]. IEEE Trans.on Power Systems, 2007, 22 (4): 1753- 1759.
5	HERD B, MILES S, MCBURNEY P, et al. Verification and validation of agent-based simulations using approximate model checking[C]//Proc.of the 14th International Workshop on Multi-Agent Systems and Agent-Based Simulation, 2013: 436-442.
6	DIMAROGONAS D V , FRAZZOLI E , JOHANSSON K H . Distributed event-triggered control for multi-agent systems[J]. IEEE Trans.on Automatic Control, 2012, 57 (5): 1291- 1297.
7	倪锦根, 马兰申. 抗脉冲干扰的分布式仿射投影符号算法[J]. 电子学报, 2016, 44 (7): 1555- 1560.
	NI J G , MA L S . Distributed affine projection sign algorithms against impulsive interferences[J]. Acta Electronica Sinica, 2016, 44 (7): 1555- 1560.
8	YAN Y M , HUANG J . Cooperative output regulation of discrete-time linear time-delay multi-agent systems under switching network[J]. Neurocomputing, 2017, 241 (7): 108- 114.
9	HASHIM H A , EL-FERIK S , LEWIS F L . Neuro-adaptive cooperative tracking control with prescribed performance of unknown higher-order nonlinear multi-agent systems[J]. International Journal of Control, 2019, 92 (2): 445- 460.
10	HE W L , CHEN G R , HAN Q L , et al. Network-based leader-following consensus of nonlinear multi-agent systems via distributed impulsive control[J]. Information Sciences, 2017, 380, 145- 148.
11	BOSSE S , LECHLEITER A . A hybrid approach for structural monitoring with self-organizing multi-agent systems and inverse numerical methods in material-embedded sensor networks[J]. Mechatronics, 2016, 34, 12- 37.
12	陶九阳, 吴琳, 胡晓峰. AlphaGo技术原理分析及人工智能军事应用展望[J]. 指挥与控制学报, 2016, 2 (2): 114- 120.
	TAO J Y , WU L , HU X F . Principle analysis of AlphaGo technology and prospect of human-engineering intelligent military application[J]. Journal of Command and Control, 2016, 2 (2): 114- 120.
13	胡晓峰, 贺筱媛, 陶九阳. AlphaGo的突破与兵棋推演的挑战[J]. 科技导报, 2017, 35 (21): 49- 60.
	HU X F , HE X Y , TAO J Y . AlphaGo's breakthrough and the challenge of military chess[J]. Science Technology Review, 2017, 35 (21): 49- 60.
14	ERNEST N , CARROLL D , SCHUMACHER C , et al. Genetic fuzzy based artificial intelligence for unmanned combat aerial vehicle control in simulated air combat missions[J]. Journal of Defense Management, 2016, 6 (1): 1000144- 1.
15	SEFFERS G I. Commanding the future mission[EB/OL].[2017-05-20]. https://www.xueshuqikanwang.com/articles_y2y12b.html.
16	GARDINER J , COVA M , NAGARAJA S . Command & control: understanding, denying and detecting[J]. Computer Science, 2015, 36 (2): 738- 741.
17	孙昱, 姚佩阳, 李明辉, 等. 兵力组织指挥控制结构适应性调整方法[J]. 系统工程与电子技术, 2016, 38 (9): 2086- 2092.
	SUN Y , YAO P Y , LI M H , et al. Adaptive adjusting method of command and control structure of army organization[J]. Systems Engineering and Electronics, 2016, 38 (9): 2086- 2092.
18	唐胜景, 史松伟, 张尧, 等. 智能化分布式协同作战体系发展综述[J]. 空天防御, 2019, 2 (1): 6- 13.
	TANG S J , SHI S W , ZHANG Y , et al. Development review of intelligent distributed cooperative combat system[J]. Air & Space Defense, 2019, 2 (1): 6- 13.
19	MOFFAT J , CAMPBELL I , GLOVER P . Validation of the mission-based approach to representing command and control in simulation models of conflict[J]. Journal of the Operational Research Society, 2004, 55 (4): 340- 349.
20	贺正洪, 张金成. 基于专家系统的防空火力分配模型[J]. 系统工程与电子技术, 2001, 23 (7): 44- 46.
	HE Z H , ZHANG J C . Air defense fire distribution model based on expert system[J]. Systems Engineering and Electronics, 2001, 23 (7): 44- 46.
21	KRUSE R , YUE W . Error analysis of randomized Runge-Kutta methods for differential equations with time-irregular coefficients[J]. Computational Methods in Applied Mathematics, 2017, 17 (3): 479- 498.
22	PAN Q , DIAS D . An efficient reliability method combining adaptive support vector machine and Monte Carlo simulation[J]. Structural Safety, 2017, 67 (4): 85- 95.

参数	数值	说明
V_TH/(m/s)	2.1	目标矢量飞行速度上限
V_TL/(m/s)	1.1	目标矢量飞行速度下限
H_TH/m	12 000	高度上界
H_TL/m	800	高度下界
RS_TH/m	8 000	航路捷径上界
RS_TL/m	-8 000	航路捷径下界
T_TH/s	120	通过目标要地的时间上界
T_TL/s	20	通过目标要地的时间下界

参数	数值	说明
α	1.6	高度威胁因子
H(m)_max	6 000	打击精度上界
H(m)_min	800	打击精度下界
β	0.8	击毁概率

编号	运输队伍	运输资源类别	运输上限	S_max	S_min
1	Team₁	O₁, O₂	500	50	75
2	Team₂	O₁, O₂	400	45	65
3	Team₃	O₃, O₄	500	45	70
4	Team₄	O₃, O₄	350	35	50
5	Team₅	O₄	400	30	60
6	Team₆	O₂, O₄	300	30	55
7	Team₇	O₁, O₂, O₃, O₄	300	45	65

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Design of command control model based on MPC-MAS under multi-platform distributed cooperative operation

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