拦截动力学及其控制
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摘要
拦截技术是军事制导领域的研究热点,也是精确拦截制导武器的关键技术。要拦截攻击各种飞行器,首先必须正确确定其飞行规律,正确预测下一时段的运动路径,对其进行正确的识别。其次必须有快速、准确攻击目标的最优制导律,从而以实现拦截目标的成功。
首先,对于拦截目标的识别而言,本文以基于轨迹拟合的目标识别预测理论为基础,将目标识别问题转化为对预测模型参数进行优化搜索的问题,建立了目标识别方法。在MAS和神经网络的基础上结合空间目标识别跟踪原理,给出了将动目标分成远、近两阶段目标的预测识别描述。提出将目标状态按自由度划分,分别进行基于MAS的神经网络子网识别预测,利用MAS和神经网络的组网原理进行目标识别的思想,并对此进行仿真验证。通过对算例的仿真,验证了基于MAS和神经网络的目标识别预测算法的正确性和有效性。
其次,拦截动力学模型建立与制导律的设计、求解是本文研究的另外一个主要内容。本文以拦截动力学理论为基础,首先介绍了拦截动力学及其几种制导律,并阐述了最优控制中的Pontryagin极小值原理,分析其优缺点及适用范围。在此基础上提出了一种拦截的新方法:即以拦截器和目标器的相对运动方程为模型,以最短时间、最少燃料消耗和最小脱靶量为综合性能指标,利用Pontryagin极小值原理进行拦截动力学制导律的设计求解。最后,给出三个典型算例,仿真结果验证了用Pontryagin极小值原理进行拦截制导律设计求解的正确性和有效性。
Intercept technology is a research hot in the military guidance field. It is also the key technology of precision intercept guided weapons. To intercept and against all kinds of vehicles, it must first correctly determine the flight rules of guided weapons, correctly estimate the next movement path, and correctly identify it. Then there must be rapid and accurate optimum guidance laws of the target attacked, so as to successful interception target.
Firstly, for intercept target identification, the paper bases on the synthesis position forecast algorithm theory, changes the target identification problem to search optimized parameters of forecast model, and gives the target identification method. It gives the forecast strategy description that the forecast divides into two kinds, the far distance target forecast and near distance target forecast based on the MAS and BP neural network and space moving target identification theory. The method dividing the target state by freedom degrees and forecasting by MAS and neural network subnet is proposed. Correctness and evaluated antigen are tested by the simulation of example.
Secondly, the interception dynamics model establishment and design of guidance law is a main content in this paper. The paper introduces the intercept dynamics and several guidance laws. Discusses the Pontryagin minimum principle of the optimal control method, and analyzes their advantages and disadvantages and applicability firstly bases on the intercept dynamics. A new algorithm based on the Pontryagin minimum principle proposed: with the relative motion equation between target and interceptor as model, in the shortest time, minimum fuel consumption and minimum miss-distance for comprehensive performance index, intercepting dynamics guidance law is designed using the Pontryagin minimum principle. Finally, it gives three typical example, the simulation results verify the correctness and effectiveness of this method.
首先,对于拦截目标的识别而言,本文以基于轨迹拟合的目标识别预测理论为基础,将目标识别问题转化为对预测模型参数进行优化搜索的问题,建立了目标识别方法。在MAS和神经网络的基础上结合空间目标识别跟踪原理,给出了将动目标分成远、近两阶段目标的预测识别描述。提出将目标状态按自由度划分,分别进行基于MAS的神经网络子网识别预测,利用MAS和神经网络的组网原理进行目标识别的思想,并对此进行仿真验证。通过对算例的仿真,验证了基于MAS和神经网络的目标识别预测算法的正确性和有效性。
其次,拦截动力学模型建立与制导律的设计、求解是本文研究的另外一个主要内容。本文以拦截动力学理论为基础,首先介绍了拦截动力学及其几种制导律,并阐述了最优控制中的Pontryagin极小值原理,分析其优缺点及适用范围。在此基础上提出了一种拦截的新方法:即以拦截器和目标器的相对运动方程为模型,以最短时间、最少燃料消耗和最小脱靶量为综合性能指标,利用Pontryagin极小值原理进行拦截动力学制导律的设计求解。最后,给出三个典型算例,仿真结果验证了用Pontryagin极小值原理进行拦截制导律设计求解的正确性和有效性。
Intercept technology is a research hot in the military guidance field. It is also the key technology of precision intercept guided weapons. To intercept and against all kinds of vehicles, it must first correctly determine the flight rules of guided weapons, correctly estimate the next movement path, and correctly identify it. Then there must be rapid and accurate optimum guidance laws of the target attacked, so as to successful interception target.
Firstly, for intercept target identification, the paper bases on the synthesis position forecast algorithm theory, changes the target identification problem to search optimized parameters of forecast model, and gives the target identification method. It gives the forecast strategy description that the forecast divides into two kinds, the far distance target forecast and near distance target forecast based on the MAS and BP neural network and space moving target identification theory. The method dividing the target state by freedom degrees and forecasting by MAS and neural network subnet is proposed. Correctness and evaluated antigen are tested by the simulation of example.
Secondly, the interception dynamics model establishment and design of guidance law is a main content in this paper. The paper introduces the intercept dynamics and several guidance laws. Discusses the Pontryagin minimum principle of the optimal control method, and analyzes their advantages and disadvantages and applicability firstly bases on the intercept dynamics. A new algorithm based on the Pontryagin minimum principle proposed: with the relative motion equation between target and interceptor as model, in the shortest time, minimum fuel consumption and minimum miss-distance for comprehensive performance index, intercepting dynamics guidance law is designed using the Pontryagin minimum principle. Finally, it gives three typical example, the simulation results verify the correctness and effectiveness of this method.
引文
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[2] Kuczewski.R .M .Neural network approaches to multi-target tracking. [A]. ICNN, 1987, 4:619-633
[3]敬忠良.神经网络跟踪理论及应用[M].北京:国防工业出版社,1995
[4]陈小惠,王建华,黄国建.基于kohonen神经网络的多传感器多目标跟踪方法[J].中国造船,2000,41(4):66-71
[5]王会利.空间作战拦截轨道设计与优化[D].西安:西北工业大学,2007,1-6
[6]袁俊.苏联/俄罗斯反卫星武器发展.世界航空航天博览B版,2005,(11):50-53
[7]“Space Missile Guidance and Control Simulation and Flight Testing”AAS-89-055
[8]“Hypervelocity Orbital Intercept Guidance Using Certainty Control”AAS-89-044
[9]反导-反飞机直接碰撞的制导与控制[J].现代防御技术,1994,第2期
[10]邓舞燕.多目标跟踪理论与方法.西北工业大学硕士论文,2002,3
[11]冯志刚,方昌华.三维极小脱靶量与极小能量综合最优控制研究与仿真.上海航天.2007,第6期,1-5
[12]李亮,和兴锁,张娟等.空间拦截最优控制的动力学建模方法研究.中国宇航学会深空探测技术专业委员会第一届学术会议.2005,1,387-390
[13] Gholson, N.H., and Moose, R.L. Maneuvering target tracking using adaptive state estimation. IEEE Transaction on Aerospace and Electronic Systems.AES_15 (May 1977), 448-456
[14]廉杰.航迹起始及机动目标跟踪方法的研究.哈尔滨工业大学硕士论文,2006
[15] [加] JIMING LIU著.靳小龙,张世武,[加]JIMING LIU译.多智能体原理与技术[M].北京:清华大学出版社,2003,1-20
[16]张维明,姚莉.智能协作信息技术[M].北京:电子工业出版社,2002,80-87,100-109
[17] [英] MICHAEL WOOLDRIDGE著.石纯一,张伟,徐晋晖等译.多AGENT系统引论.北京:电子工业出版社,2003,3-10
[18]王学通,王伟,于蕾,王理.多Agent系统研究概述[J].现代电子技术.2006,10(225),65-67
[19] HE Yan-xiang, CHENG Xin-meng.The design and application of Agent and multi-Agent [M].Wuhan: Wuhan University Press, 2001
[20]黄敏,佟振声.分布式多Agent系统的研究[J].电力情报.2002,(4),65-70
[21]石纯一,黄昌宁,王家广编著.人工智能原理[M].北京:清华大学出版社,1993
[22] Jennings, N.R., Syeara, etal. A Roadmap of Agent Research and Development. Autonomous Agents and Multi-Agent Systems Journal.1998, 1(1):7-38P
[23]高隽编著.人工神经网络原理及仿真实例[M].北京:机械工业出版社,2003.7
[24]田达章.基于神经网络的飞机目标识别研究.哈尔滨工程大学硕士学位论文.2007,57-65
[25]飞思科技产品研发中心.神经网络理论与MATLAB7实现.北京:电子工业出版社,2005.3
[26]周立伟.目标探测与识别[M].北京:北京理工大学出版社,2004.9
[27]周宏仁,敬忠良,王培德.机动目标跟踪[M].北京:国防工业出版社,1991
[28]侯明,王培德.机动多目标的模型与跟踪算法.航空学报,1990,第5期,282-287
[29]杨青智.机动目标跟踪技术的研究[D].西安:西安电子科技大学,2008.1
[30]龙英睿.空间动目标预测跟踪研究[D].西安:西安电子科技大学,2007.1
[31]朱自谦.一种用于机动目标跟踪的复合机动模型及其应用[C].控制与决策年会论文集,1992,513-518
[32]韩丽,史丽萍等.基于多Agent系统设计原理的神经网络结构设计[J].中国矿业大学,2007
[33]郭成昊,赵颜利,刘凤玉.多Agent自主体系结构的建模与分析[J].南京理工大学,2005
[34]赵锋娟.目标识别与位置估计融合处理的多Agent系统[D].哈尔滨工程大学,2006.12
[35] An Architecture for Adaptive Intelligent Systems Artificial Intelligence: Special Issue on Agents and Interactivity.1995, 72:329-365P
[36] Terabe, M.Wasio, etal. A Study of Organizational Learning in Multi-agent Systems. Distributed Artificial Intelligence Meets Machine Learning. 1997:168-179P
[37]祝斌.直升机稳瞄操作控制系统的实现及无人机航迹仿真研究[D].南京航空航天大学,2001.3
[38]叶庆凯,王肇明.优化与最优控制中的计算方法[M].科学出版社,1986
[39]李忠应.最优过程理论及其在飞行力学中的应用[J].北京:北京航空学院出版社.1990年12月,184-191
[40]王朝珠,秦化淑.最优控制理论[M].北京:科学出版社,2003
[41]刘豹,唐万生主编.现代控制理论[M].北京:机械工业出版社,2006.7
[42]徐士良.计算机常用算法[M].清华大学出版社,1989.11.1
[43]陈宝林.最优化理论与算法[M].北京:清华大学出版社,2003
[44]程国采.航天飞行器最优控制理论与方法[M].北京:国防工业出版社,1999
[45] Scheel WA, Conway B.A. Optimization of very-low-thrust, many-revolution Spacecraft trajectories. Jounral of Guidance, Control, and Dynamics, 1994, 17(6): 1185-1927
[46] LAWDEN D F.Optimal trajectories for space navigation [M].London: Buter Worths, 1963
[47] CATERT.Effects of propel least mass loss on fuel-optimal rendezvous near Keperian orbit [J].Journal of Guidance, Control and Dynamics,1989,12(1):74-78
[48]方昌华.基于小推力的空间平台机动拦截最优控制律与拦截状态评估[D].国防科学技术大学,2007,1-27
[49]陆亚东,杨明,郑扬飞等.拦截弹作战指挥系统建模研究[J].哈尔滨工业大学
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