相对漂浮船舶动力定位的跟踪控制方法研究
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摘要
现在动力定位(Dynamic Positioning,DP)系统在研究和应用上都已经非常成熟,但关于相对水面动目标的动力定位技术在国内还不曾具体研究过。本文提出了相对漂浮船舶动力定位的跟踪控制的概念,不是传统的定点定位和循迹跟踪,而是要求定位船跟踪一个在海面上自由漂浮的目标船,使两船始终保持相对固定的位置和艏向,并对该技术进行初步研究。
本文建立了动力定位船舶的低频运动和高频运动数学模型、推进器模型、测量模型,同时对风、浪、流等外界环境扰动的数学模型进行描述。自由漂浮船采用了与定位船相同的数学模型进行仿真。给出了定轴推力器与全回转推力器的布置以及推力分配策略。
介绍了数字式PID的控制算法,给出了神经网络的学习方式、网络结构等有关概念,着重分析了有关BP网络和对角回归网络(DRNN)的特性。结合相对自由漂浮运动船舶的动力定位系统设计了基于神经网络的BP—PID和DRNN—PID两种控制器用来对定位船加以控制。最后结合所设计的控制器进行仿真研究。同时将DRNN—PID控制器跟BP—PID控制器的控制效果进行比较。最后,通过对系统的仿真结果可以看出,DRNN—PID控制器具有控制精度比较高、响应时间比较短及稳定性比较性强的特点,对不同海流的干扰具有一定的适应能力。同时也验证了本文系统模型的正确性和准确性,表明该控制方法具有一定的理论意义及实用价值。
Nowadays, the research on dynamic positioning system (DPS) is becoming more and more advanced. But the dissertation about the offshore target with random movement is not involved. The offshore ship inferred is freely floating in the control of the environment effect. The dissertation puts forward the idea about the research on the tracking control of the dynamic positioning system relative to a freely floating vessel. It keeps the DP ship with floating ship in the fixed location and heading, while the traditional DPS characteristic is that the DPS offshore target is a fixed location or track. If the method could be used in the practice, it will contribute to the exploitation of the ocean and the aggiornamento of the navy.
A simple motion low frequency and high frequency mathematical model of vessel are established. The thruster, measurement and environment including wind wave and current models are also established. The floating ship adopts the model as the same as the DP ship and is simulated in the control of the environment.
After the presentation of the digital PID algorithm, the neutral network framework is introduced, and the BP algorithm Diagonal Recurrent Neural Network's characteristic is presented. As the new DPS is considered, the application of BP based PID and DRNN based PID hybrid inversed control techniques is designed and analyzed. Comparing the arithmetic of hybrid control, we can see that the DRNN—PID controller has the feature of high precision, quick-response, and high stability, and it also can be used in numerous environment. Finally, the correctness and accuracy of the DP model are validated, which showed that this method has high value for theory and utility.
本文建立了动力定位船舶的低频运动和高频运动数学模型、推进器模型、测量模型,同时对风、浪、流等外界环境扰动的数学模型进行描述。自由漂浮船采用了与定位船相同的数学模型进行仿真。给出了定轴推力器与全回转推力器的布置以及推力分配策略。
介绍了数字式PID的控制算法,给出了神经网络的学习方式、网络结构等有关概念,着重分析了有关BP网络和对角回归网络(DRNN)的特性。结合相对自由漂浮运动船舶的动力定位系统设计了基于神经网络的BP—PID和DRNN—PID两种控制器用来对定位船加以控制。最后结合所设计的控制器进行仿真研究。同时将DRNN—PID控制器跟BP—PID控制器的控制效果进行比较。最后,通过对系统的仿真结果可以看出,DRNN—PID控制器具有控制精度比较高、响应时间比较短及稳定性比较性强的特点,对不同海流的干扰具有一定的适应能力。同时也验证了本文系统模型的正确性和准确性,表明该控制方法具有一定的理论意义及实用价值。
Nowadays, the research on dynamic positioning system (DPS) is becoming more and more advanced. But the dissertation about the offshore target with random movement is not involved. The offshore ship inferred is freely floating in the control of the environment effect. The dissertation puts forward the idea about the research on the tracking control of the dynamic positioning system relative to a freely floating vessel. It keeps the DP ship with floating ship in the fixed location and heading, while the traditional DPS characteristic is that the DPS offshore target is a fixed location or track. If the method could be used in the practice, it will contribute to the exploitation of the ocean and the aggiornamento of the navy.
A simple motion low frequency and high frequency mathematical model of vessel are established. The thruster, measurement and environment including wind wave and current models are also established. The floating ship adopts the model as the same as the DP ship and is simulated in the control of the environment.
After the presentation of the digital PID algorithm, the neutral network framework is introduced, and the BP algorithm Diagonal Recurrent Neural Network's characteristic is presented. As the new DPS is considered, the application of BP based PID and DRNN based PID hybrid inversed control techniques is designed and analyzed. Comparing the arithmetic of hybrid control, we can see that the DRNN—PID controller has the feature of high precision, quick-response, and high stability, and it also can be used in numerous environment. Finally, the correctness and accuracy of the DP model are validated, which showed that this method has high value for theory and utility.
引文
[1]M.J.摩根.近海船舶的动力定位[M].北京:国防工业出版社,1985.
[2]ABS.Guide for thrusters and dynamic Positioning systems,1995.
[3]A Laugh.Dynamic positioning[J].Lloyd's Register Technical Association,1985.
[4]D F Philips Bask(Hones).The dynamic positioning of ships:the problems solved[A].UKACC International Conference on Control'96[C].IEE,1996.
[5]Ingénues U.Simulation of low frequency motions of dynamically positioned offshore structures[J].Royal Institution of Naval Architects(London)Supplementary Papers,1987,129:127-145.
[6]Johan Winchers,Rebound van Disk.Benefits of using assisted DP for deepwater mooring systems[A].Offshore Technology Conference[C].1996.
[7]LiuDing-yuInfraredLaserTechniquesforShipborneNavigationApplications[A]China Japan Joint Meeting on Microwave'95[c].Dali an:Dali an Maritime Universes.1995.437-444.
[8]Balchen,J.G.,A.Jessen and 5.Saelid.Dynamic Positioning Using Kalman Filtering and Optimal Control Theory,IFAC/ IFIP SymR On Austin Offshore Oil Field Operation Holland,Amsterdam,1976:183-186P
[9]Balchen.J.G.,N.A.Jessen and 5.Saelid.Dynamic Positioning of locating Vessels Based on Kalman Filtering and Optimal Control of the 19th IEEE Conf.on Decision and Control,Albuquerque,,N.M.1980:852-864P
[10]Balchen.J.G.,N.A.Jessen,E.Mathisen and 5.Saelid.A Dynamic positioning System Based on Kalman Filtering and Optimal Control.Modeling dandification and control 1980 1(3) 135-163P
[11]Grimble,M.J.R.J.Patton and D.A.Wise.The Design of Dynamic Ship Positioning Control Systems Using Stochastic Optimal Control theory.1980,(1):167-171.
[12]Grimble,M.J,R.J.Patton and D.A.Wise.Use of Kalman Filtering techniques in Dynamic Ship Positioning Systems.IEE Proc.1980,127(3):93-102.
[13]D F Philips Bess(Hones).The dynamic positioning of ships:the Problems solved? UKACC International Conference on Control'96[C].IEE.1996.
[14]王丽娟,李英辉,赵希人.模糊控制技术在船舶动力定位中的应用研究.船舶工程,1999(3):8-11页.
[15]周岗,周永余,陈永冰.基于模糊理论的舰船航迹控制器.海军工程大学学报,2000(3):47-51页.
[16]周其节,徐建闽.神经网络控制系统的研究与展望.1992,Vol.9(6).
[17]孙增沂等.神经网络逆控制理论与技术,清华大学出版社,北京,1997.
[18]谷丽丽,邓志良.船舶动力定位中的模糊控制器优化技术.舰船电子工程,2006(3):114-116页.
[19]赵志高,杨建民.动力定位系统发展状况及研究方法.海洋工程.2002,20(1):91-97.
[20]王晓声.船舶动力定位系统设计及实验研究[J].
[21]Makoto Hocus,Masashi Kashiwagi,Wataru Koterayama.Hydrodynamics of a depth controlled tower vehicle[A].日本造船学会秋季演讲会において讲演[C].
[22]庞永杰,徐玉如,倪绍毓.船舶在复杂海况下的实时运动仿真[J].船舶工程,1998,(1):12-15.
[23]倪绍毓.波浪漂移力的数值模拟[J].海洋工程,1992,10(2):30-31.
[24]杨建民,顾海粟.非接触式六自由度运动测量与分析方法[J].海洋工程,1999,17(2):17-21.
[25]李殿璞.船舶运动与建模.哈尔滨工程大学出版社.1999.
[26]徐丽娜.神经网络控制.电子工业出版社,2003:1-5.
[27]王科俊,王克成.神经网络建模、预报与控制.哈尔滨工程大学出版社,1996:3-5.
[28]于卫红,贾传荧.BP神经网络在救助船选优中的应用.计算机工程,2006年24期.
[29]黄山松,杜继宏,冯元馄.人工神经网络及其在控制中的应用,计算机自动测量与控制,1999,Vol.7(2):57-59.
[30]李捷,王伟智.基于神经网络逆控制的自适应控制器设计.福建电脑,2004,10:23-25.
[31]李仁俊.基于神经网络的自适应逆控制研究.昆明理工大学硕士学位论文2003.
[32]潘永湘,吴转峰,李强.一种基于串联BP算法的神经网络自适应逆控制方法.控制与决策,2002,17(增刊2):70-72.
[33]Sorensen,A.J.Sagatun,S.I.And Fossen.The.Design of a dynamic Positioning system using model based control,Proceedings of the 3rd IFAC worship on control applications in marine systems,Sondheim,Norway,1995:16-26.
[34]Triantafyllou,M.S.,M.Bodson and M.Athans.Real Time Estimation of Ship Motions using Kalman Filtering Techniques.IEEE Journal of Oceanic Engineering,1983 JOE-8(1):3-15.
[35]Fossen,Thor I.Guidance and control of ocean vehicles[J].Chi Chester New York,1995.
[36]王科俊,王克成.神经网络建模、预报与控制,第一版,哈尔滨.哈尔滨工程大学出版社,1996.
[37]陈雪丽,程启明.船舶航迹保持的神经网络控制器研究.河南大学学报,29(5),2001,70-73.
[38]覃方君,李安等.模拟退火优化BP神经网络在航向控制系统中的应用.海军工程大学学报,第17卷,第2期,2005,100-103.
[39]杜刚,战兴群等.基于神经网络的非线性船舶航向自适应逆控制.测控技术.2005,24(4),23-26.
[40]冯纯伯,罗宁苏,李晓明.自适应控制系统的鲁棒性,自动化学报,VOL.13,N0.6,1987:463-472.
[41]Austrom K J.Directions in Intelligent Control.Plenary Session,IFAC International Symposium,ITAC91(Intelligent Tuning and Adaptive Control),1991:15-17.
[42]Miehael Nikolaou,Vijay Kumar Hanagandi."Dynamic Process modeling with recurrent neural networks"A1ChEJ,1993,V39(10):1654-1667.
[43]Youy,NikolaouM."Dynamic Process modeling with referent neural networks",AICHEJ,1993,Vol.39:1654-1660.
[44]朱群雄,孙锋.RNN神经网络的应用研究,北京化工学报,1998V25(1):86-90.
[45]Linguini,MadanM.GuPta,Fellow,IEEE "Stable Dynamic Back Propagation Learning in Recurrent Neural Networks",IEEE Transactions Ural Networks 1999,Vol.IO(6):1321-1232.
[46]赵英凯,熊辉,蔡宁.基于DRNN的非线性对象的状态预估及其加速算法,计算技术与自动化,1999,V18(1):22-25.
[47]姜华.船舶动力定位时的运动估计与控制.哈尔滨工程大学硕士学位论文.1996.
[48]王卫红,于镭,姚广.基于对角回归神经网络的转台伺服系统逆控制.系统工程与电子技术,2008,27(8):1456-1458.
[49]韩璞,张海琳,张丽静.神经网络自适应逆控制的仿真研究.华北电力大学学报,2001,28(3):26-30.
[50]Bahrami M,Tait K E.Design of direct controller of PID type by receptive field neural networks.IJCNN'03-Nago-ya:2003.1809-1912.
[51]王耀南,蔡自兴.基于神经元网络的智能PID控制及应用.信息与控制,1994,23(3):185-189.
[52]谭永红.神经网络自适应PID控制及其应用.模式识别与人工智能,1993,6(1):81-85.
[2]ABS.Guide for thrusters and dynamic Positioning systems,1995.
[3]A Laugh.Dynamic positioning[J].Lloyd's Register Technical Association,1985.
[4]D F Philips Bask(Hones).The dynamic positioning of ships:the problems solved[A].UKACC International Conference on Control'96[C].IEE,1996.
[5]Ingénues U.Simulation of low frequency motions of dynamically positioned offshore structures[J].Royal Institution of Naval Architects(London)Supplementary Papers,1987,129:127-145.
[6]Johan Winchers,Rebound van Disk.Benefits of using assisted DP for deepwater mooring systems[A].Offshore Technology Conference[C].1996.
[7]LiuDing-yuInfraredLaserTechniquesforShipborneNavigationApplications[A]China Japan Joint Meeting on Microwave'95[c].Dali an:Dali an Maritime Universes.1995.437-444.
[8]Balchen,J.G.,A.Jessen and 5.Saelid.Dynamic Positioning Using Kalman Filtering and Optimal Control Theory,IFAC/ IFIP SymR On Austin Offshore Oil Field Operation Holland,Amsterdam,1976:183-186P
[9]Balchen.J.G.,N.A.Jessen and 5.Saelid.Dynamic Positioning of locating Vessels Based on Kalman Filtering and Optimal Control of the 19th IEEE Conf.on Decision and Control,Albuquerque,,N.M.1980:852-864P
[10]Balchen.J.G.,N.A.Jessen,E.Mathisen and 5.Saelid.A Dynamic positioning System Based on Kalman Filtering and Optimal Control.Modeling dandification and control 1980 1(3) 135-163P
[11]Grimble,M.J.R.J.Patton and D.A.Wise.The Design of Dynamic Ship Positioning Control Systems Using Stochastic Optimal Control theory.1980,(1):167-171.
[12]Grimble,M.J,R.J.Patton and D.A.Wise.Use of Kalman Filtering techniques in Dynamic Ship Positioning Systems.IEE Proc.1980,127(3):93-102.
[13]D F Philips Bess(Hones).The dynamic positioning of ships:the Problems solved? UKACC International Conference on Control'96[C].IEE.1996.
[14]王丽娟,李英辉,赵希人.模糊控制技术在船舶动力定位中的应用研究.船舶工程,1999(3):8-11页.
[15]周岗,周永余,陈永冰.基于模糊理论的舰船航迹控制器.海军工程大学学报,2000(3):47-51页.
[16]周其节,徐建闽.神经网络控制系统的研究与展望.1992,Vol.9(6).
[17]孙增沂等.神经网络逆控制理论与技术,清华大学出版社,北京,1997.
[18]谷丽丽,邓志良.船舶动力定位中的模糊控制器优化技术.舰船电子工程,2006(3):114-116页.
[19]赵志高,杨建民.动力定位系统发展状况及研究方法.海洋工程.2002,20(1):91-97.
[20]王晓声.船舶动力定位系统设计及实验研究[J].
[21]Makoto Hocus,Masashi Kashiwagi,Wataru Koterayama.Hydrodynamics of a depth controlled tower vehicle[A].日本造船学会秋季演讲会において讲演[C].
[22]庞永杰,徐玉如,倪绍毓.船舶在复杂海况下的实时运动仿真[J].船舶工程,1998,(1):12-15.
[23]倪绍毓.波浪漂移力的数值模拟[J].海洋工程,1992,10(2):30-31.
[24]杨建民,顾海粟.非接触式六自由度运动测量与分析方法[J].海洋工程,1999,17(2):17-21.
[25]李殿璞.船舶运动与建模.哈尔滨工程大学出版社.1999.
[26]徐丽娜.神经网络控制.电子工业出版社,2003:1-5.
[27]王科俊,王克成.神经网络建模、预报与控制.哈尔滨工程大学出版社,1996:3-5.
[28]于卫红,贾传荧.BP神经网络在救助船选优中的应用.计算机工程,2006年24期.
[29]黄山松,杜继宏,冯元馄.人工神经网络及其在控制中的应用,计算机自动测量与控制,1999,Vol.7(2):57-59.
[30]李捷,王伟智.基于神经网络逆控制的自适应控制器设计.福建电脑,2004,10:23-25.
[31]李仁俊.基于神经网络的自适应逆控制研究.昆明理工大学硕士学位论文2003.
[32]潘永湘,吴转峰,李强.一种基于串联BP算法的神经网络自适应逆控制方法.控制与决策,2002,17(增刊2):70-72.
[33]Sorensen,A.J.Sagatun,S.I.And Fossen.The.Design of a dynamic Positioning system using model based control,Proceedings of the 3rd IFAC worship on control applications in marine systems,Sondheim,Norway,1995:16-26.
[34]Triantafyllou,M.S.,M.Bodson and M.Athans.Real Time Estimation of Ship Motions using Kalman Filtering Techniques.IEEE Journal of Oceanic Engineering,1983 JOE-8(1):3-15.
[35]Fossen,Thor I.Guidance and control of ocean vehicles[J].Chi Chester New York,1995.
[36]王科俊,王克成.神经网络建模、预报与控制,第一版,哈尔滨.哈尔滨工程大学出版社,1996.
[37]陈雪丽,程启明.船舶航迹保持的神经网络控制器研究.河南大学学报,29(5),2001,70-73.
[38]覃方君,李安等.模拟退火优化BP神经网络在航向控制系统中的应用.海军工程大学学报,第17卷,第2期,2005,100-103.
[39]杜刚,战兴群等.基于神经网络的非线性船舶航向自适应逆控制.测控技术.2005,24(4),23-26.
[40]冯纯伯,罗宁苏,李晓明.自适应控制系统的鲁棒性,自动化学报,VOL.13,N0.6,1987:463-472.
[41]Austrom K J.Directions in Intelligent Control.Plenary Session,IFAC International Symposium,ITAC91(Intelligent Tuning and Adaptive Control),1991:15-17.
[42]Miehael Nikolaou,Vijay Kumar Hanagandi."Dynamic Process modeling with recurrent neural networks"A1ChEJ,1993,V39(10):1654-1667.
[43]Youy,NikolaouM."Dynamic Process modeling with referent neural networks",AICHEJ,1993,Vol.39:1654-1660.
[44]朱群雄,孙锋.RNN神经网络的应用研究,北京化工学报,1998V25(1):86-90.
[45]Linguini,MadanM.GuPta,Fellow,IEEE "Stable Dynamic Back Propagation Learning in Recurrent Neural Networks",IEEE Transactions Ural Networks 1999,Vol.IO(6):1321-1232.
[46]赵英凯,熊辉,蔡宁.基于DRNN的非线性对象的状态预估及其加速算法,计算技术与自动化,1999,V18(1):22-25.
[47]姜华.船舶动力定位时的运动估计与控制.哈尔滨工程大学硕士学位论文.1996.
[48]王卫红,于镭,姚广.基于对角回归神经网络的转台伺服系统逆控制.系统工程与电子技术,2008,27(8):1456-1458.
[49]韩璞,张海琳,张丽静.神经网络自适应逆控制的仿真研究.华北电力大学学报,2001,28(3):26-30.
[50]Bahrami M,Tait K E.Design of direct controller of PID type by receptive field neural networks.IJCNN'03-Nago-ya:2003.1809-1912.
[51]王耀南,蔡自兴.基于神经元网络的智能PID控制及应用.信息与控制,1994,23(3):185-189.
[52]谭永红.神经网络自适应PID控制及其应用.模式识别与人工智能,1993,6(1):81-85.