ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Original Paper

A three dimensional robust guidance law design based on RBF neural network gain adjustment

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.04.004
  • Received Date: 30 September 2014
  • Accepted Date: 06 March 2015
  • Rev Recd Date: 06 March 2015
  • Publish Date: 30 April 2015
  • By adopting the three dimensional nonlinear model for the relative motion of missiles and targets,a scheme of guidance law was presented. The theoretical basis of the guidance law includes input-to-state stability (ISS) as well as the dynamic adjustment and self-study ability of the radial basis function (RBF) neural network. The control law is capable of dynamically adjusting the gain of nonlinear guidance law with the angular rate change of LOS (line of sight). The guidance law can avoid the undershoot augment caused by gain fixation and large-scale target-maneuvering, and also effectively trace as well as intercept the target making a variety of maneuvers. The numerical simulation results demonstrate the adaptivity and easy implementation of the control law.
    By adopting the three dimensional nonlinear model for the relative motion of missiles and targets,a scheme of guidance law was presented. The theoretical basis of the guidance law includes input-to-state stability (ISS) as well as the dynamic adjustment and self-study ability of the radial basis function (RBF) neural network. The control law is capable of dynamically adjusting the gain of nonlinear guidance law with the angular rate change of LOS (line of sight). The guidance law can avoid the undershoot augment caused by gain fixation and large-scale target-maneuvering, and also effectively trace as well as intercept the target making a variety of maneuvers. The numerical simulation results demonstrate the adaptivity and easy implementation of the control law.
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  • [1]
    刘兴堂, 周自全, 李为民, 等. 现代导航、制导与测控技术[M]. 北京: 科学出版社, 2010.
    [2]
    孙胜, 张华明, 周荻. 末端导引律综述[J]. 航天控制, 2012, 30(1): 86-96.
    Sun S, Zhang H M, Zhou D. A survey of terminal guidance law[J]. Aerospace Control, 2012, 30(1): 86-96.
    [3]
    Murtaugh S A, Criel H E. Fundamentals of proportional navigation[J]. IEEE Spectrum, 1966, 3(12): 75-85.
    [4]
    Yang C D, Yang C C. A unified approach to proportional navigation[J]. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(2): 557-567.
    [5]
    Yang C D, Chen H Y. Nonlinear H∞ robust guidance law for homing missiles [J]. Journal of Guidance Control and Dynamics, 1998, 21(6): 882-890.
    [6]
    Oshman Y, Rad D A. Differential-game-based guidance law using target orientation observations [J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(1): 316-326.
    [7]
    佘文学, 周凤岐. 三维非线性变结构寻的制导律[J].宇航学报, 2004, 25(6): 681-685.
    She W X, Zhou F Q. High precision 3-D nonlinear variablestructure guidance law for homing missile[J]. Journal of Astronautics, 2004, 25(6): 681-685.
    [8]
    Khalil H K. Nonlinear Systems[M]. 3rd, London: Prentice-Hall; 北京: 电子工业出版社, 2007.
    [9]
    严晗,季海波.一种针对机动目标的三维鲁棒导引律[J].控制工程,2011, 18(3): 393-396.
    Yan H,Ji H B. Three-dimensional robust nonlinear guidance laws against maneuvering target[J]. Control Engineering of China, 2011, 18(3): 393-396.
    [10]
    李士勇, 章钱. 智能制导:寻的导弹智能自适应导引律[M]. 哈尔滨: 哈尔滨工业大学出版社, 2011.
    [11]
    Lu S W, Basar T. Robust non-linear system identification using neural-network models[J]. IEEE Transactions on Neural Networks, 1998, 9(3): 407-429.
    [12]
    Chen F C, Khalil H K. Adaptive control of nonlinear systems using neural networks[J]. International Journal of Control, 1991, 55(6): 1299-1317.
    [13]
    陈超, 罗德林, 沈春林. 径向基神经网络在优化导引律中的应用[J]. 飞行设计, 2006, (4): 50-53.
    [14]
    方群, 王祥. 基于在线RBF神经网络的BTT导弹控制器设计[J]. 西北工业大学学报, 2014, 32(3): 446-450.
    Fang Q, Wang X.Designing BTT missile flight controller with on-line RBF neural network[J]. Journal of Northwestern Polytechnical University, 2014, 32(3): 446-450.
    [15]
    李国庆. 两种基于RBF神经网络的自适应模糊导引律[C]// 上海市红外与遥感学会2008年学术年会. 上海, 2008.
    [16]
    严晗, 季海波. 具有输入动态不确定性的三维鲁棒非线性导引律[C]// 第29届中国控制会议. 北京, 2010: 6179-6185.
    [17]
    张泽旭. 神经网络控制与Matlab仿真[M]. 第一版, 哈尔滨: 哈尔滨工业大学出版社,2011.
    [18]
    章钱, 李士勇. 一种新型自适应RBF神经网络滑模制导律[J]. 智能系统学报, 2009, 4(4): 339-344.
    Zhang Q, Li S Y. A new adaptive RBFNN sliding mode guidance law for intercepting maneuvering targets[J]. CAAI Transactions on Intelligent Systems,2009, 4(4): 339-344.
    [19]
    周灿辉, 周德云, 张堃. 攻击机动目标的导弹三维变结构导引律[J]. 电光与控制, 2012, 19(6): 17-20.
    Zhou C H, Zhou D Y, Zhang K. A 3D variable structure guidance law for missiles in attacking maneuvering targets[J]. Electronics Optics & Control,2012, 19(6): 17-20.)
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Catalog

    [1]
    刘兴堂, 周自全, 李为民, 等. 现代导航、制导与测控技术[M]. 北京: 科学出版社, 2010.
    [2]
    孙胜, 张华明, 周荻. 末端导引律综述[J]. 航天控制, 2012, 30(1): 86-96.
    Sun S, Zhang H M, Zhou D. A survey of terminal guidance law[J]. Aerospace Control, 2012, 30(1): 86-96.
    [3]
    Murtaugh S A, Criel H E. Fundamentals of proportional navigation[J]. IEEE Spectrum, 1966, 3(12): 75-85.
    [4]
    Yang C D, Yang C C. A unified approach to proportional navigation[J]. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(2): 557-567.
    [5]
    Yang C D, Chen H Y. Nonlinear H∞ robust guidance law for homing missiles [J]. Journal of Guidance Control and Dynamics, 1998, 21(6): 882-890.
    [6]
    Oshman Y, Rad D A. Differential-game-based guidance law using target orientation observations [J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(1): 316-326.
    [7]
    佘文学, 周凤岐. 三维非线性变结构寻的制导律[J].宇航学报, 2004, 25(6): 681-685.
    She W X, Zhou F Q. High precision 3-D nonlinear variablestructure guidance law for homing missile[J]. Journal of Astronautics, 2004, 25(6): 681-685.
    [8]
    Khalil H K. Nonlinear Systems[M]. 3rd, London: Prentice-Hall; 北京: 电子工业出版社, 2007.
    [9]
    严晗,季海波.一种针对机动目标的三维鲁棒导引律[J].控制工程,2011, 18(3): 393-396.
    Yan H,Ji H B. Three-dimensional robust nonlinear guidance laws against maneuvering target[J]. Control Engineering of China, 2011, 18(3): 393-396.
    [10]
    李士勇, 章钱. 智能制导:寻的导弹智能自适应导引律[M]. 哈尔滨: 哈尔滨工业大学出版社, 2011.
    [11]
    Lu S W, Basar T. Robust non-linear system identification using neural-network models[J]. IEEE Transactions on Neural Networks, 1998, 9(3): 407-429.
    [12]
    Chen F C, Khalil H K. Adaptive control of nonlinear systems using neural networks[J]. International Journal of Control, 1991, 55(6): 1299-1317.
    [13]
    陈超, 罗德林, 沈春林. 径向基神经网络在优化导引律中的应用[J]. 飞行设计, 2006, (4): 50-53.
    [14]
    方群, 王祥. 基于在线RBF神经网络的BTT导弹控制器设计[J]. 西北工业大学学报, 2014, 32(3): 446-450.
    Fang Q, Wang X.Designing BTT missile flight controller with on-line RBF neural network[J]. Journal of Northwestern Polytechnical University, 2014, 32(3): 446-450.
    [15]
    李国庆. 两种基于RBF神经网络的自适应模糊导引律[C]// 上海市红外与遥感学会2008年学术年会. 上海, 2008.
    [16]
    严晗, 季海波. 具有输入动态不确定性的三维鲁棒非线性导引律[C]// 第29届中国控制会议. 北京, 2010: 6179-6185.
    [17]
    张泽旭. 神经网络控制与Matlab仿真[M]. 第一版, 哈尔滨: 哈尔滨工业大学出版社,2011.
    [18]
    章钱, 李士勇. 一种新型自适应RBF神经网络滑模制导律[J]. 智能系统学报, 2009, 4(4): 339-344.
    Zhang Q, Li S Y. A new adaptive RBFNN sliding mode guidance law for intercepting maneuvering targets[J]. CAAI Transactions on Intelligent Systems,2009, 4(4): 339-344.
    [19]
    周灿辉, 周德云, 张堃. 攻击机动目标的导弹三维变结构导引律[J]. 电光与控制, 2012, 19(6): 17-20.
    Zhou C H, Zhou D Y, Zhang K. A 3D variable structure guidance law for missiles in attacking maneuvering targets[J]. Electronics Optics & Control,2012, 19(6): 17-20.)

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