基于补偿滑模神经网络的某炮控系统位置控制
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摘要
针对某炮控系统存在较强的非线性和不确定性特征,提出了基于补偿滑模的自组织神经网络控制策略。引入了补偿滑模面设计方法,构成了自组织神经网络控制器和辅助补偿器。自组织神经网络控制器由Hermite多项式、变结构神经网络和神经元参数自学习算法构成,其减小了计算复杂度,提高了自适应能力;梯度下降法对神经网络的参数进行自学习,提高了系统的收敛速度;辅助补偿器的引入进一步减小了系统稳态误差,满足了该炮控系统的基本指标要求,保证了系统在Lyapunov意义下的稳定性和鲁棒性。半实物仿真试验表明:该控制策略有效地提高了系统的控制精确度和鲁棒性,减小了外界干扰对系统性能的影响。
A self-organizing neural network with complementary sliding modes control strategy is proposed for the strong nonlinearities and uncertainties of a gun control system( GCS),which consists of the selforganizing neural network controller( SNNC) and the auxiliary compensation controller( ACC) with the complementary sliding mode surface. The self-organizing neural network controller included a Hermite polynomial,a variable structure self-organizing neural network( VSSNN) and self-learning parameters with the gradient descent method,which reduced the computational complexity and accelerated the ability of adaptation. The gradient descent method adjusted parameters of the neural network and promoted the convergence rapidity. The auxiliary compensator was introduced to further reduce steady-state error of the system,which satisfied the basic indicators of requirements and guaranteed the stability and robustness of the system in the sense of Lyapunov. The semi-physical test simulation shows that the control strategy greatly improves the control accuracy and robustness of the system,and effectively eliminates the influ-ence of disturbance in the system.
A self-organizing neural network with complementary sliding modes control strategy is proposed for the strong nonlinearities and uncertainties of a gun control system( GCS),which consists of the selforganizing neural network controller( SNNC) and the auxiliary compensation controller( ACC) with the complementary sliding mode surface. The self-organizing neural network controller included a Hermite polynomial,a variable structure self-organizing neural network( VSSNN) and self-learning parameters with the gradient descent method,which reduced the computational complexity and accelerated the ability of adaptation. The gradient descent method adjusted parameters of the neural network and promoted the convergence rapidity. The auxiliary compensator was introduced to further reduce steady-state error of the system,which satisfied the basic indicators of requirements and guaranteed the stability and robustness of the system in the sense of Lyapunov. The semi-physical test simulation shows that the control strategy greatly improves the control accuracy and robustness of the system,and effectively eliminates the influ-ence of disturbance in the system.
引文
[1]马晓军,王福兴,袁东.全电式炮控系统非线性特性及其控制策略[J].装甲兵工程学院学报,2011,25(1):63.MA Xiaojun,WANG Fuxing,YUAN Dong.Nonlinearity characteristics and its control strategies of all-electric tank gun control system[J].Journal of Academy of Armored Force Engineering,2011,25(1):63.
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[3]张文静,台宪青.基于Lu Gre模型的火炮伺服系统摩擦力矩自适应补偿[J].清华大学学报,2007,47(2):1756.ZHANG Wenjing,TAI Xianqing.Adaptive friction compensation in gun servo systems based on the Lu Gre model[J].Journal of Tsinghua University,2007,47(2):1756.
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[5]颜景斌,王飞,王美静,等.改进滑模变结构控制光伏系统最大功率点跟踪[J].哈尔滨理工大学学报,2016,21(4):106.YAN Jingbing,WANG Fei,WANG Meijing,et al.Improved sliding mode control of maximum power point tracking of solar photovoltaic systems[J].Journal of Harbin University of Science and Technology,2016,21(4):106.
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[7]冯亮,马晓军,冯东,等.坦克炮控伺服系统的滑模非线性摩擦补偿控制[J].火力与指挥控制,2008,33(12):63.FENG Liang,MA Xiaojun,FENG Dong,et al.Sliding mode nonlinear friction compensation control of gun control servo system of tank[J].Fire Control and Command Control,2008,33(12):63.
[8]宋清昆,刘一.免疫遗传算法小波神经网络控制器设计[J].哈尔滨理工大学学报,2015,20(4):55.SONG Qingkun,LIU Yi.Immune genetic algorithm of wavelet neural network controller design[J].Journal of Harbin University of Science and Technology,2015,20(4):55.
[9]冯亮,马晓军,闫之峰.坦克稳定器的神经滑模控制方法[J].装甲兵工程学院学报,2006,20(5):61.FENG Liang,MA Xiaojun,YAN Zhifeng.Method of neural network sliding mode control of tank stabilizer[J].Journal of Academy of Armored Force Engineering,2006,20(5):61.
[10]马晓军,冯亮,袁东.坦克炮控系统非线性特性及自适应补偿控制[J].火力与指挥控制,2010,35(11):1.MA Xiaojun,FENG Liang,YUAN Dong.Overview of adaptive compensation control of nonlinearity in the tank gun control system[J].Fire Control and Command Control,2010,35(11):1.
[11]马晓军,袁东,臧克茂,等.数字全电式坦克炮控系统研究现状与发展[J].兵工学报,2012,32(1):70.MA Xiaojun,YUAN Dong,ZANG Kemao,et al.Research on situation and development of digital all-electrical gun control system of tank[J].Acta Armamentarii,2012,32(1):70.
[12]于靖,陈谋,姜长生.基于干扰观测器的非线性不确定系统自适应滑模控制[J].控制理论与应用,2014,31(8):993.YU Jing,CHEN Mou,JIANG Changsheng.Adaptive sliding mode control for nonlinear uncertain systems based on disturbance observer[J].Control Theory&Applications,2014,31(8):993.
[13]叶镭,夏元清,付梦印,等.无人炮塔炮控系统自抗扰控制[J].控制理论与应用,2014,31(11):1580.YE Lei,XIA Yuanqing,FU Mengyin et al.Active disturbance rejection control for gun control system of unmanned turret[J].Control Theory&Applications,2014,31(11):1580.
[14]郭犇.电动负载模拟器的控制系统研究[D].哈尔滨:哈尔滨工业大学,2012:31.
[15]高强,候润明,杨国来,等.基于分数阶神经滑模的某顶置火炮调炮控制[J].兵工学报,2013,34(10):1311.GAO Qiang,HOU Runmin,YANG Guolai,et al.Adjustment and control of a certain top-mounted gun based on a novel fractional order neural sliding mode strategy[J].Acta Armamentarll,2013,34(10):1311.
[16]CHUNFEI H,BOREKUEN L.FPGA-based adaptive PID control of a DC motor driver via sliding-mode approach[J].Expert Systems with Applications,2011,38(9):11866.
[17]CHUNCHIEH W,JUHNGPERNG S.Composite sliding mode control of chaotic systems with uncertainties[J].International Journal of Bifurcation and Chaos,2003,13(4):863.
[18]SLOTINEJEANJACQUES E,WEIPING L.Applied nonlinear control[D].Prentice-Hall,Englewood Cliffs,NJ,1991:301.
[19]CHUNFEI H.Intelligent control of chaotic systems via self-organizing Hermite-polynomial-based neural network[J].Neurocomputing,2014,123(0):197.
[20]CHUNFEI H,Self-organizing adaptive fuzzy neural control for a class of nonlinear systems,IEEE Transactions on Neural Networks,2007,18(4):1232.
[21]CHIHMIN L,HSINYI L.Self-organizing adaptive wavelet CMAC backstepping control system design for nonlinear chaotic systems[J].Nonlinear Analysis:Real World Applications,2013,14(1):206.
[22]CHUNFEI H.A self-evolving functional-linked wavelet neural network for control applications[J].Applied Soft Computing,2013,13(11):4392.
[2]李匡成.坦克炮控系统齿隙非线性建模与补偿控制策略分析[J].微特电机,2010,6(1):45.LI Kuangcheng.Modeling and compensation control analysis of backlash nonlinearity in gun control system of tanks[J].Small&Special Electrical Machines,2010,6(1):45.
[3]张文静,台宪青.基于Lu Gre模型的火炮伺服系统摩擦力矩自适应补偿[J].清华大学学报,2007,47(2):1756.ZHANG Wenjing,TAI Xianqing.Adaptive friction compensation in gun servo systems based on the Lu Gre model[J].Journal of Tsinghua University,2007,47(2):1756.
[4]蔡建平,沈陆娟.坦克炮控伺服系统未知摩擦的自适应补偿控制[J].火力与指挥控制,2013,38(4):64.CAI Jianping,SHEN Lujuan.Adaptive compensation of unknown friction for gun control servo system of tank[J].Fire Control&Command Control,2013,38(4):64.
[5]颜景斌,王飞,王美静,等.改进滑模变结构控制光伏系统最大功率点跟踪[J].哈尔滨理工大学学报,2016,21(4):106.YAN Jingbing,WANG Fei,WANG Meijing,et al.Improved sliding mode control of maximum power point tracking of solar photovoltaic systems[J].Journal of Harbin University of Science and Technology,2016,21(4):106.
[6]冯亮,马晓军,闫之峰,等.坦克炮控系统自适应模糊滑模控制方法[J].电机与控制学报,2007,11(1):65.FENG Liang,MA Xiaojun YAN Zhifeng,et al.Method of adaptive fuzzy sliding mode control of gun control system of tank[J].Electric Machines and Control,2007,11(1):65.
[7]冯亮,马晓军,冯东,等.坦克炮控伺服系统的滑模非线性摩擦补偿控制[J].火力与指挥控制,2008,33(12):63.FENG Liang,MA Xiaojun,FENG Dong,et al.Sliding mode nonlinear friction compensation control of gun control servo system of tank[J].Fire Control and Command Control,2008,33(12):63.
[8]宋清昆,刘一.免疫遗传算法小波神经网络控制器设计[J].哈尔滨理工大学学报,2015,20(4):55.SONG Qingkun,LIU Yi.Immune genetic algorithm of wavelet neural network controller design[J].Journal of Harbin University of Science and Technology,2015,20(4):55.
[9]冯亮,马晓军,闫之峰.坦克稳定器的神经滑模控制方法[J].装甲兵工程学院学报,2006,20(5):61.FENG Liang,MA Xiaojun,YAN Zhifeng.Method of neural network sliding mode control of tank stabilizer[J].Journal of Academy of Armored Force Engineering,2006,20(5):61.
[10]马晓军,冯亮,袁东.坦克炮控系统非线性特性及自适应补偿控制[J].火力与指挥控制,2010,35(11):1.MA Xiaojun,FENG Liang,YUAN Dong.Overview of adaptive compensation control of nonlinearity in the tank gun control system[J].Fire Control and Command Control,2010,35(11):1.
[11]马晓军,袁东,臧克茂,等.数字全电式坦克炮控系统研究现状与发展[J].兵工学报,2012,32(1):70.MA Xiaojun,YUAN Dong,ZANG Kemao,et al.Research on situation and development of digital all-electrical gun control system of tank[J].Acta Armamentarii,2012,32(1):70.
[12]于靖,陈谋,姜长生.基于干扰观测器的非线性不确定系统自适应滑模控制[J].控制理论与应用,2014,31(8):993.YU Jing,CHEN Mou,JIANG Changsheng.Adaptive sliding mode control for nonlinear uncertain systems based on disturbance observer[J].Control Theory&Applications,2014,31(8):993.
[13]叶镭,夏元清,付梦印,等.无人炮塔炮控系统自抗扰控制[J].控制理论与应用,2014,31(11):1580.YE Lei,XIA Yuanqing,FU Mengyin et al.Active disturbance rejection control for gun control system of unmanned turret[J].Control Theory&Applications,2014,31(11):1580.
[14]郭犇.电动负载模拟器的控制系统研究[D].哈尔滨:哈尔滨工业大学,2012:31.
[15]高强,候润明,杨国来,等.基于分数阶神经滑模的某顶置火炮调炮控制[J].兵工学报,2013,34(10):1311.GAO Qiang,HOU Runmin,YANG Guolai,et al.Adjustment and control of a certain top-mounted gun based on a novel fractional order neural sliding mode strategy[J].Acta Armamentarll,2013,34(10):1311.
[16]CHUNFEI H,BOREKUEN L.FPGA-based adaptive PID control of a DC motor driver via sliding-mode approach[J].Expert Systems with Applications,2011,38(9):11866.
[17]CHUNCHIEH W,JUHNGPERNG S.Composite sliding mode control of chaotic systems with uncertainties[J].International Journal of Bifurcation and Chaos,2003,13(4):863.
[18]SLOTINEJEANJACQUES E,WEIPING L.Applied nonlinear control[D].Prentice-Hall,Englewood Cliffs,NJ,1991:301.
[19]CHUNFEI H.Intelligent control of chaotic systems via self-organizing Hermite-polynomial-based neural network[J].Neurocomputing,2014,123(0):197.
[20]CHUNFEI H,Self-organizing adaptive fuzzy neural control for a class of nonlinear systems,IEEE Transactions on Neural Networks,2007,18(4):1232.
[21]CHIHMIN L,HSINYI L.Self-organizing adaptive wavelet CMAC backstepping control system design for nonlinear chaotic systems[J].Nonlinear Analysis:Real World Applications,2013,14(1):206.
[22]CHUNFEI H.A self-evolving functional-linked wavelet neural network for control applications[J].Applied Soft Computing,2013,13(11):4392.