潜艇水下悬停运动建模与操纵控制技术研究
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摘要
潜艇作为最为重要的武器承载平台之一,在现代战争中发挥着不可替代的重要战略作用。潜艇的操纵性能是潜艇最为重要的性能之一,在执行某些特殊任务的过程中,潜艇需要在航行停车状态下,对潜艇的深度和纵倾姿态进行控制,称为潜艇水下悬停操纵。良好的潜艇水下悬停操纵可以降低潜艇自身噪声,减少水下耗电量,因而具有重要的战略意义。然而我国对于潜艇水下悬停操纵自动控制的研究还处于起步阶段,因而有大量的基础性研究工作需要完成。本文在深入研究潜艇操纵运动建模与控制技术发展现状以及水下悬停操纵运动控制特点的基础之上,系统地研究潜艇水下悬停运动机理、辨识建模技术以及操纵控制策略。论文主要研究内容如下：
首先介绍了潜艇水下运动建模参考坐标系体系以及基于欧拉角法的坐标系转换法则,推导了潜艇垂直面运动方程；在此基础之上,分析了潜艇水下悬停运动的运动特性,建立了潜艇水下悬停运动模型；对悬停操纵执行机构—悬停水舱的工作机理进行了分析,建立了浮力调节水舱供气吹除过程数学模型；以某型潜艇为例计算悬停运动水动力参数,对所建立的潜艇水下悬停模型进行了验证。
出于精细控制与悬停控制策略仿真环境构建的目的,系统地完成了对包括初始不均衡量、艇体体积压缩、密度变化干扰、近水面波浪干扰以及海流在内的水下悬停环境干扰力建模；介绍了在悬停环境干扰建模基础上设计的潜艇悬停操纵环境仿真平台设计方案。
针对潜艇水下悬停运动机理建模方法存在的不足,提出了基于扩展随机减量技术的潜艇水下悬停运动辨识建模技术。研究了随机减量技术应用的局限性,提出了应用条件更为宽松的扩展随机减量技术,推导了潜艇水下悬停扩展随机减量方程,并以此为基础设计了一种基于加权拟线性回归算法和多层感知器神经网络相结合的混合网络系统辨识技术。在不需要获得系统实时输入数据的条件下,通过扩展随机减量技术计算辨识系统的输入样本,将潜艇水下悬停运动建模工作分为系统阻尼参数,恢复参数辨识和系统耦合参数辨识两部分,分别通过加权拟线性回归算法和多层感知器神经网络独立完成辨识工作。实验结果表明,在绝大多数情况下,本文的混合网络辨识方法都可以提供响应预测误差小于5%的潜艇潜艇水下悬停运动模型。
潜艇水下悬停运动过程本质上是一个弱机动,慢时变的动态过程,这为基于系统模型的线性控制方法的应用提供了足够的依据,实际的工程应用也证明了这一点。出于实际应用中对系统自调整实时性要求的考虑,提出了基于快速TS模糊模型的FTFM技术,设计了基于FTFM的潜艇水下悬停解耦模糊PID控制算法。在系统解耦的基础之上,设计潜艇水下悬停模糊自适应PID控制器,使得潜艇水下悬停控制系统在两个被控维度上的子系统都具有参数自调整能力,能够在线辨识PID参数调整模糊规则。实验结果表明设计的解耦模糊PID控制器无论在控制精度,系统时效性,还是模糊规则规模控制方面都表现出良好的特性。
出于即避开复杂的潜艇运动建模和干扰力建模过程的考虑,采用不依赖被控对象模型的控制方法来设计潜艇水下悬停操纵控制器。设计了一种基于神经网络表述形式的模糊逻辑控制系统,即具有参数自适应和结构学习功能的模糊神经控制器,称为模糊自适应神经网络控制(Fuzzy Adaptive Neuro-Networks Control, FANC)。FANC系统通过神经网络的连接结构实现该模糊系统从输入到输出变量的映射,采用一种5层前向网络结构,集合了FLS的知识表达和推理能力,ANN的知识获取、学习及适应能力。设计了一种分为自组织和监督学习两个阶段的混合学习算法对FANC系统进行训练。针对潜艇水下悬停操纵这一时变、强耦合和不确定的复杂非线性多输入多输出被控过程,设计了潜艇水下悬停操纵FANC系统以及相应的网络训练算法。实验表明FANC应用到潜艇水下悬停操纵控制中可以取得良好的控制效果。
Submarine is one of the most important platforms for carrying the weapons, which play an irreplaceable important strategic role in modern warfare. Maneuvering performance is one of the most important performances of submarines. In the implementation of some special missions, a submarine needs to control depth and pitch attitude in parking state, which is known as the submarine hovering control. Good hovering performance can reduce the noises of a submarine, and reduce the electricity consumption, which has an important strategic significance. However, research of the automatic control for submarine hovering is still in its initial stage, so there are a lot of basic researches need to be done. In this paper, based on a in-depth study of the development status of the motion modeling and maneuvering control strategy, a systematic study of submarine hovering movement mechanism and identification modeling technology and hovering maneuvering control strategy is carried on. Major research jobs in the paper are as followed:
First of all, the underwater motion modeling reference coordinates system and the transforming law of the coordinates was introduced, and the motion equation of a submarine moving in the vertical plane was derved; the mechanism of the submarine hovering tanks was analized to establish a mathematical model for the buoyancy regulating tanks in the submarine hovering control system. Finally, the movement kinematics of a hovering submarine was analized to establish the hovering motion model of a submarine, and the model was validated.
In order to get better control performance and building the simulation environment, the models of the interferences was derived, including the initial imbalances, the hull volume compression, the density changes of the seawater, the surface wave interferences and the ocean currents. The design of environment simulation platform for the maneuvering control of a submarine was described on the base of the modeling of the environmental interferences.
Taking into account the shortcomings of the mechanism modeling approaches, a submarine hovering motion identification modeling technique based on an extended random decrement technique was proposed. First, the limitation of the classic random decrement technique was researched, and an extended random decrement technique with more liberal application conditions was proposed, and the extended random decrement equation of a hovering submarine was derived. Based on the extended random decrement technique, a hybrid network system identification technique based on weighted fitting linear regression algorithm and multilayer perceptron neural networks was designed, which is named Weighted Fitting Linear Regression Neural Network algorithm (WFLRNN). The input samples of the identification system was obtained with the extended random decrement technique without measuring the real-time input datas of the recognized system, the identification of the submarine hovering motion was divided into the identification of the damping parameters, recovery parameters and the identification of the coupling parameters, these two parts of identification jobs were finished by the weighted fitting linear regression algorithm and a MLP neural network independently. Experimental results show that both in the case of broadband or narrowband excitation, WFLRNN method can provide identificaed submarine hovering models with errors no more than5%.
Submarine hover is essentially a weak motor, slow time-varying dynamic process, which provides sufficient basis for the application of the linear control strategies which are based on the system models. On the base of the decoupling of the system, a fuzzy PID submarine hovering controller whose subsystems in the two dimensions of the hovering motion can make self-tuning by identifing the PID parameters online was designed. For taking the real-time requirements in practical applications into account, a fast TS fuzzy modeling technology called FTFM was proposed. After all these preparing jobs, a decoupling fuzzy PID control system was designed based the FTFM for the hovering control of a submarine. Experimental results show that the designed decoupling fuzzy PID controller showed good properties not only in control accuracy, timeliness, but also in the size of fuzzy control rules.
Considering complexity of the modeling of the motion and the interferences during the process, take the control methods which are not rely on the system modeling into consideration to manipulate the submarine hovering controller. A fuzzy logic control system with a neural network-based presentated structure whose structure and parameters are adaptive was designed, which was called Fuzzy Adaptive Neuro-Networks Control system (FANC). The FANC system was connected with a5-layer feedforward neural network to implement the fuzzy variables mapping from input to output. The system is a collection of FLS knowledge representation and reasoning and ANN knowledge acquisition, learning and adaptability. Meanwhile a mixed learning algorithm for the learning of FANC system was proposed, which is divided into a self-organized stage and a supervised learning stage. Finally a FANC system with the corresponding training algorithm for the submarine hovering controlling was designed. Experiments show that the FANC system applied to submarine hovering control can achieve good control performance.
首先介绍了潜艇水下运动建模参考坐标系体系以及基于欧拉角法的坐标系转换法则,推导了潜艇垂直面运动方程；在此基础之上,分析了潜艇水下悬停运动的运动特性,建立了潜艇水下悬停运动模型；对悬停操纵执行机构—悬停水舱的工作机理进行了分析,建立了浮力调节水舱供气吹除过程数学模型；以某型潜艇为例计算悬停运动水动力参数,对所建立的潜艇水下悬停模型进行了验证。
出于精细控制与悬停控制策略仿真环境构建的目的,系统地完成了对包括初始不均衡量、艇体体积压缩、密度变化干扰、近水面波浪干扰以及海流在内的水下悬停环境干扰力建模；介绍了在悬停环境干扰建模基础上设计的潜艇悬停操纵环境仿真平台设计方案。
针对潜艇水下悬停运动机理建模方法存在的不足,提出了基于扩展随机减量技术的潜艇水下悬停运动辨识建模技术。研究了随机减量技术应用的局限性,提出了应用条件更为宽松的扩展随机减量技术,推导了潜艇水下悬停扩展随机减量方程,并以此为基础设计了一种基于加权拟线性回归算法和多层感知器神经网络相结合的混合网络系统辨识技术。在不需要获得系统实时输入数据的条件下,通过扩展随机减量技术计算辨识系统的输入样本,将潜艇水下悬停运动建模工作分为系统阻尼参数,恢复参数辨识和系统耦合参数辨识两部分,分别通过加权拟线性回归算法和多层感知器神经网络独立完成辨识工作。实验结果表明,在绝大多数情况下,本文的混合网络辨识方法都可以提供响应预测误差小于5%的潜艇潜艇水下悬停运动模型。
潜艇水下悬停运动过程本质上是一个弱机动,慢时变的动态过程,这为基于系统模型的线性控制方法的应用提供了足够的依据,实际的工程应用也证明了这一点。出于实际应用中对系统自调整实时性要求的考虑,提出了基于快速TS模糊模型的FTFM技术,设计了基于FTFM的潜艇水下悬停解耦模糊PID控制算法。在系统解耦的基础之上,设计潜艇水下悬停模糊自适应PID控制器,使得潜艇水下悬停控制系统在两个被控维度上的子系统都具有参数自调整能力,能够在线辨识PID参数调整模糊规则。实验结果表明设计的解耦模糊PID控制器无论在控制精度,系统时效性,还是模糊规则规模控制方面都表现出良好的特性。
出于即避开复杂的潜艇运动建模和干扰力建模过程的考虑,采用不依赖被控对象模型的控制方法来设计潜艇水下悬停操纵控制器。设计了一种基于神经网络表述形式的模糊逻辑控制系统,即具有参数自适应和结构学习功能的模糊神经控制器,称为模糊自适应神经网络控制(Fuzzy Adaptive Neuro-Networks Control, FANC)。FANC系统通过神经网络的连接结构实现该模糊系统从输入到输出变量的映射,采用一种5层前向网络结构,集合了FLS的知识表达和推理能力,ANN的知识获取、学习及适应能力。设计了一种分为自组织和监督学习两个阶段的混合学习算法对FANC系统进行训练。针对潜艇水下悬停操纵这一时变、强耦合和不确定的复杂非线性多输入多输出被控过程,设计了潜艇水下悬停操纵FANC系统以及相应的网络训练算法。实验表明FANC应用到潜艇水下悬停操纵控制中可以取得良好的控制效果。
Submarine is one of the most important platforms for carrying the weapons, which play an irreplaceable important strategic role in modern warfare. Maneuvering performance is one of the most important performances of submarines. In the implementation of some special missions, a submarine needs to control depth and pitch attitude in parking state, which is known as the submarine hovering control. Good hovering performance can reduce the noises of a submarine, and reduce the electricity consumption, which has an important strategic significance. However, research of the automatic control for submarine hovering is still in its initial stage, so there are a lot of basic researches need to be done. In this paper, based on a in-depth study of the development status of the motion modeling and maneuvering control strategy, a systematic study of submarine hovering movement mechanism and identification modeling technology and hovering maneuvering control strategy is carried on. Major research jobs in the paper are as followed:
First of all, the underwater motion modeling reference coordinates system and the transforming law of the coordinates was introduced, and the motion equation of a submarine moving in the vertical plane was derved; the mechanism of the submarine hovering tanks was analized to establish a mathematical model for the buoyancy regulating tanks in the submarine hovering control system. Finally, the movement kinematics of a hovering submarine was analized to establish the hovering motion model of a submarine, and the model was validated.
In order to get better control performance and building the simulation environment, the models of the interferences was derived, including the initial imbalances, the hull volume compression, the density changes of the seawater, the surface wave interferences and the ocean currents. The design of environment simulation platform for the maneuvering control of a submarine was described on the base of the modeling of the environmental interferences.
Taking into account the shortcomings of the mechanism modeling approaches, a submarine hovering motion identification modeling technique based on an extended random decrement technique was proposed. First, the limitation of the classic random decrement technique was researched, and an extended random decrement technique with more liberal application conditions was proposed, and the extended random decrement equation of a hovering submarine was derived. Based on the extended random decrement technique, a hybrid network system identification technique based on weighted fitting linear regression algorithm and multilayer perceptron neural networks was designed, which is named Weighted Fitting Linear Regression Neural Network algorithm (WFLRNN). The input samples of the identification system was obtained with the extended random decrement technique without measuring the real-time input datas of the recognized system, the identification of the submarine hovering motion was divided into the identification of the damping parameters, recovery parameters and the identification of the coupling parameters, these two parts of identification jobs were finished by the weighted fitting linear regression algorithm and a MLP neural network independently. Experimental results show that both in the case of broadband or narrowband excitation, WFLRNN method can provide identificaed submarine hovering models with errors no more than5%.
Submarine hover is essentially a weak motor, slow time-varying dynamic process, which provides sufficient basis for the application of the linear control strategies which are based on the system models. On the base of the decoupling of the system, a fuzzy PID submarine hovering controller whose subsystems in the two dimensions of the hovering motion can make self-tuning by identifing the PID parameters online was designed. For taking the real-time requirements in practical applications into account, a fast TS fuzzy modeling technology called FTFM was proposed. After all these preparing jobs, a decoupling fuzzy PID control system was designed based the FTFM for the hovering control of a submarine. Experimental results show that the designed decoupling fuzzy PID controller showed good properties not only in control accuracy, timeliness, but also in the size of fuzzy control rules.
Considering complexity of the modeling of the motion and the interferences during the process, take the control methods which are not rely on the system modeling into consideration to manipulate the submarine hovering controller. A fuzzy logic control system with a neural network-based presentated structure whose structure and parameters are adaptive was designed, which was called Fuzzy Adaptive Neuro-Networks Control system (FANC). The FANC system was connected with a5-layer feedforward neural network to implement the fuzzy variables mapping from input to output. The system is a collection of FLS knowledge representation and reasoning and ANN knowledge acquisition, learning and adaptability. Meanwhile a mixed learning algorithm for the learning of FANC system was proposed, which is divided into a self-organized stage and a supervised learning stage. Finally a FANC system with the corresponding training algorithm for the submarine hovering controlling was designed. Experiments show that the FANC system applied to submarine hovering control can achieve good control performance.
引文
[1]施生达.潜艇操纵性.北京：国防工业出版社,1995.10-49
[2]潘国良.潜艇水下悬停操纵原理研究.研究报告,707研究所九江分部,2004
[3]船舶水动力学词典[M].中国造船编辑部译.上海：中国造舟船编辑部,1981
[4]潘国良.潜艇水下悬停运动的操纵控制研究.东南大学硕士论文.2005
[5]潘国良.潜艇水下悬停及其发展综述.中国造船工程学会2006年船舶通讯导航学术会议,2006：216-221
[6]Richard R J, Stoten D P. Depth Control of a Submersible Vehicles. ISP. 1981,28(318):30-39
[7]孙元泉,马运义等.潜艇和深潜器的现代操纵理论与应用.国防工业出版社,2001
[8]Abkowitz, M.A.,1980. Measurement of Hydrodynamic Characteristics from Ship Maneuvering Trails by System Identification. SNAME Transactions 88:283-318
[9]东昉.刘正江.数字航海.大连：大连海事大学出版社,2010：96-100
[10]Gertler, Hagan. Standard equation of motion for submarine simulation. NSDRC Report 2510,1967
[11]Feidman J. DTNSRDC Revised Standard Submarine Equations of Motion. DTNSRDC Report SPD-0393-09,1979.6
[12]李世保,刘常波,彭达胜等.潜艇六自由度运动仿真器.计算机仿真,1996.1
[13]Drurey L. Automatic control of submarine depth, Pitch and trim. MSEE Thesis, Naval Postgraduate School,Monterey,CA,1975
[14]易炜,迟焕德,张本文.潜艇空间运动线性方程研究.舰船科学技术,2002,6
[15]Joonyoung Kim, Kihun Kim, et aL. Estimation of Hydrodynamic Coefficients for an AUV Using Nonlinear Observers. IEEE Journal of oceanic Engineering,2002, 27(4):830-840
[16]M. M. A. Pourzanjani, H. K. Zienkiewicz, J. O. Flower. A Hybrid of Estimating Hydrodynamically-Generated Forces for Use in Ship-Maneuvering Simulation. ISP, 1987,34:207-216
[17]Feldman J. Straightline and rotating arm captive-model experiments to investigate the stability and control characteristics of submarines and other submerged vehicles. AD-A209575,1987
[18]Feldman J. Method of performing captive-model experiments to predict the stability and control characteristics of submarines. AD-A297814,1995
[19]Lewandouski E M.A new apparatus for captive model testing of submerged bodies in conig motion.23rd ATTC,1993:295-299
[20]Mackay M. Submarine turning circle experiments in a towing tank. International Symposium on Naval submarine,1996
[21]Brownell W F. A rotating arm and maneuvering basin. DTMB Report 1053,1959
[22]Watt G D, et al.Submarine hydrodynamics in the wind tunnel:the DREA static test rig. Warship'91 International Symposium on Naval Submarines,1996
[23]Roddv R, et al.Conceptual design of a new planar motion mechanism for investigating the stability and control characteristics of submarines. AD-A297800, 1995
[24]N S Y. Hydrodynamic forces on a moving submerged body in waves. International Shipbuilding Progress,1994,41:426
[25]Wardle A B. Prediction of normal force, pitching moment and yawing force on bodies of revolution at angles of attack up to 50 degrees using a concentrated vortex flow-field model. AD-A775894,1973
[26]Boudet L. Low frequency vertical forces action on a submerged body:A comparison between theory and experiments. Hydrodynamics Computations model tests and reality,1992:315-320
[27]Cotma A, Broudin J, Renhas Y H. Le nouveau modele libre de sous-matin du Bassin d'Essais des Carenes. Bulletin de I'ATMA,1990,(90):703-733
[28]Renhas Y H, Lempereur C, Mathe J M. Les essays de modele libre de sous-marins en basin de traction:un nouvreau outil pour I'tude hydronamique du sous-marim. Bulletin de I'ATMA,1993,(93)
[29]David Taylor Research Center. Straightline and rotating arm captive-model experiments to investigate the stability and control characteristics of submarines and other submerged vehicles. AD-A2095750XSP,1987,11
[30]Giovanni Indiveri. Modeling and Identification of Underwater Robotic Systems. PHD. Thesis, University of Genova, Genova, Italy, December 1998
[31]D. E. Leader. Kalman Filter Estimation of Underwater Vehicle Position and Attitude Using a Doppler Velocity Aided Inertial Motion Unit. PHD. Thesis, MIT,1994
[32]H. J. Bohlmal.An Analytical Method for the Prediction of submarine Maneuverability. Warship'91 International Symposiumon Naval Submannes,1991
[33]Lioyd A. Progress towards a Rational Method of Predicting Submarine Maneuvers. The Proeeedings of the RINA Iniernational Symposium,1983
[34]W. faller, D. Hess, et al. Applications of recursive neural network technologies to hydrodynamics.22th symposium on Naval Hydrodynamics,2000:705-723
[35]Isherwood R M. Wind resistance of merchant ship Trans.of RINA. No.115,1973
[36]杨盐生.定长风下舵力保向舵角的计算.大连海运学院学报,13(4),1987
[37]Barr R. A. A review and comparison of ship maneuvering methods. SNAME transaction,1993
[38]戴余良,刘祖源,俞科云.潜艇应急上浮操纵与控制研究评述.舰船科学技术,2007,12,15
[39]Bakakin A V, et al. Control algorithms for VSSs:in VSSs and their use in flight automation proc. Nauka, Moscow,1968
[40]Emelyanov S V. Variable structure control system. Nauka, Moscow,1967
[41]Utikin V I. Sliding mode and their application in VSSs. Moscow,1978
[42]Itkis U. Control systems of variable structure. John Wiley&Sons,1976
[43]冯俊梅,连琏,葛彤.潜艇操纵控制方法的现状与发展.海洋工程,2005,23(1)：114-122
[44]Cristi R, Papoulias F A, Healey A J. Adaptive sliding mode control of autonomous underwater vehicles in the dive plane. IEEE, Oceanic Eng,1990,15(3):245-250
[45]赵国良,杨玉红.潜艇垂直面机动的多变量控制.中国造船,1989,(4)：107-116
[46]牟军,许汉珍.潜艇操纵运动的变结构控制.华中理工大学学报,1997,6
[47]牟军.潜艇操纵运动的变结构控制系统研究.华中理工大学博士学论文,1997
[48]金鸿章,王科俊,梁超.具有横摇补偿控制的潜艇航向变结构控制.中国造船,2001
[49]杨惠民.国外核潜艇技术综述.潜艇,1986(1)：115-121
[50]R. K. Lea, Allen S. L. Merry. A comparative study of control techniques for an underwater flight vehicle. Int. J. System Science,1990(9):947-964
[51]H.R.叶费姆也夫.潜艇理论基础.北京：国防工业出版社,1975
[52]施生达.潜艇原理.武汉：海军工程大学出版社,1990
[53]潘国良.舰船操纵控制技术应用与分析.声学与电子工程,2005(增刊)：6-9
[54]何卫华等.潜艇自动操纵控制系统的基本原理及其现状.舰船科学技术,2005,27(3)：20-24
[55]W. F. Klepsen, et al. A study of the impact of SSN 688 Class Submarine Design on a Generalized Advanced Casualty ship Control Training Device.(AD764096), Jun. 1973
[56]Richard A. Stankey. "Trident Ship Control"System Development Program. (AD/AO45603),1975
[57]Michael G. Daughan. Seawolf Submarine Ship Control System:A Case Study of a Fault-Tolerant Design. Naval Engineers Journal, Feb.1994
[58]AD-272913.弹道导弹潜艇的先进船控操纵台.美国通用动力公司,1961
[59]P.巴里诶.潜艇水下悬停时自动定深装置的设计与制造.法国航海与航空技术协会(?)ATMA),1970, NO.70:327-345
[60]王淑英.潜艇操纵控制系统自动化发展研究.“十五”科研发展计划参考资料,中国舰船研究院.1999
[61]施生达.关于潜艇操纵及其自动化技术的近期发展问题探讨.海军工程大学,2001
[62]陈训铨.潜艇操纵自动化的现状及发展.舰船操纵自动化技术专题文,2001
[63]王学峰.常规潜艇水下悬停操纵及战术思考.潜艇42支队,2001
[64]王科俊,姚绪梁,金鸿章.海洋运动体控制原理.哈尔滨工程大学出版社,2005：260-272
[65]吴秀恒,刘祖源,施生达等.船舶操纵性.北京：国防工业出版社,2005
[66]李殿璞.船舶运动与建模.哈尔滨工程大学出版社,1999：102-131
[67]Ahmad S M, Sutton R. Linear, Nonlinear Modeling and AUV retrieval. Plymouth: The University of Plymouth,2000
[68]常文君,刘建成,于华南,徐玉如.水下机器人运动控制与仿真的数学模型.船舶工程.2002,3：58-60
[69]毛斌宏.潜艇空间运动模型及均衡控制技术的研究.哈尔滨工程大学硕士论文,20019
[70]王晓东.潜艇潜浮系统仿真研究初探.舰船科学技术,2004,26(1)：14-19
[71]戴余良.潜艇应急上浮操纵运动分析与控制技术研究.武汉：武汉理工大学博士学位论文,2007.5：14-20
[72]季鲁慧.潜艇运动多变量控制系统研究..哈尔滨船舶工程学院硕士论文,1989：66-70
[73]郝英泽,何斌.海水密度变化对潜艇悬停垂向运动的影响仿真研究.船海工程,第37卷,第2期,2008,4：117-119
[74]齐霄强.潜器悬浮运动模型及控制方法研究.哈尔滨工程大学硕士论文,2008,2
[75]缪泉明,冯学知,孙伯起.圆柱体在近水面的波浪力计算.中国船舶科学研究中心科技报告,1992
[76]缪国平,刘应中,糜振星.斜浪中二维潜体的二阶定常力.中国造船,1994,(125)：20-31
[77]缪国平,刘应中,糜振星.二阶波浪力的切片理论与潜体上的垂向定常力.水动力学 研究与进展,1993,8
[78]缪国平,刘应中,王海平.细长体上的二阶波浪力的切片理论.自然科学进展,1996,5
[79]冯学知,孙伯起,缪泉明.潜艇近水面波浪扰动力研究.中国船舶科学研究中心科技报告,1996
[80]Lee C M, Newman J N. First-and second-order wave effects on a submerged spheroid. J.S.R.1991,35(3):183-19OP
[81]AD-A1429281
[82]王鹢,王文武,孙枫,王国夫.于扰力作用下潜艇近水面运动的仿真.系统仿真学报,2003,15(1)：84-87
[83]邱大洪.波浪理论及其在工程上的应用.北京：高等教育出版社,1985：74-90
[84]Slavesen N. Second-order steady-state forces and moments on surface ship oblique regular wave. Int. Symp. On the Dynamics of the Marine Vehicles and structures in waves. London,1974:212-216
[85]Willmott P. On the motion of a slender body submerged beneath surface wave. J.S.R.1988,32(3)
[86]O'Dea J F, Barr R A. Prediction of wave-induced first order motions and second order forces and moments during submarine maneuvers. Technical Report 7611-1, Hydronautics, Inc,1976,9
[87]Crook T P. An initial assessment of free surface effects on submerged bodies. Master's Thesis, Naval Postgraduate School,1994,9
[88]闵耀元,闫学琳.波浪中潜艇近水而操纵控制规律.719所科技报告,2000
[89]金鸿章.长峰波随机海浪的实时仿真和频谱分析.船舶工程,1987,2(1)：24-30
[90]冯学知,蒋强强,缪泉明,匡晓峰.潜体波浪中近水面不同潜深和航向时运动和波浪力计算.船舶力学,2002,6(2)：1-14
[91]贾欣乐,杨盐生.船舶运动数学模型-机理建模及辨识建模.大连：大连海事大学出版社,1999
[92]COLE H. A. On-the-line analysis of random vibration. AIAA/ASME 9th Structure, Structural Dynamics and Materials Conference,1968
[93]COL E H. A. Method and apparatus for measuring the damping characteristic of a structure. United State:3,620,069,1971
[94]Haddara, M.R., and Xu, J. On the Use of Random Decrement in the Identification of Two Degrees of Freedom Systems, Proceedings of CSME FORUM. Ryerson Polytechnic University. Toronto, May 19-22, vol.4,1998:499-507
[95]陈德成,姜节胜.随机减量技术的方法与理论.振动与冲击,1984,3(4)：31-39
[96]Haddara, M.R. On the Random Decrement for Nonlinear Rolling Motion. Proceedings of the 12th International Conference on Offshore Mechanics and Arctic Engineering, Calgary, Canada,1992:283-288
[97]Mahfouz, A.B. Identification of the Hydrodynamic Model of an Underwater Robotic Vehicle Heaving and Pitching Near the Sea Surface Using its Measured Response, Ph.D. Dissertation, Faculty of Engineering and Applied Science, Memorial University of Newfoundland,2001
[98]R. E. Reed. Analytical Aspects of Randomdec Analysis. AIAA.20,1979
[99]Haddara, M., and Wang, Y. Parametric Identification of Coupled Sway and Yaw Motions, International Conference on Offshore Mechanics and Arctic Engineering, vol.1, Offshore Technology,1996:267-273
[100]Mahfouz, A.B., Haddara, M.R., Williams, C.D. Prediction of the Hydrodynamic Parameters in the Coupled Heave-Pitch Motion Equations for Underwater Robotic Vehicles Using Measured Response at Sea. Journal of Offshore Mechanics and Arctic Engineering 2001,(12)3:1-10
[101]Montgomery D. Design and Analysis of Experiments. New York:John Wiley&Sons,1997
[102]Wang, Y. Ship Maneuverability Prediction Using Neural Networks, M. Eng. thesis, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, 1996
[103]李岳锋.随机减量技术原理探讨.南京航空学院学报,No.2,1984
[104]Yuh, J. "Development in Underwater Robotics," Proc.,IEEE International Conference on Robotics and Automation, Piscataway, NJ,1995:1862-1867
[105]Yuh J. "Control of Underwater Robotic Vehicles," Proc., International Conference on Intelligent Robots and Systems, Yokohama, Japan, July,1993:517-521
[106]张景绘,唐照千.对随机减量技术的数学表示.应用力学学报,VOL.1,No.2,1984
[107]Conte, G, Serrani, A. "Modeling and Simulation of Underwater Vehicles," Proc., IEEE International Symposium on Computer-Aided Control System Design, Dearborn, MI,1996:62-67
[108]Lloyd, A. R. J. M. SEAKEEPING:Ship Behavior in Rough Weather, Ellis Harwood Limited,1989
[109]Bhattacharyya, R. Dynamics of Marine Vehicles, John Wiley & Sons Inc., New York, NY,1978
[110]胡耀华,贾欣乐.具有约束条件的船舶运动预测控制.控制理论与应用, 2000,17(4):452-457
[111]Bingham, H., Korsmeyer, F., Newman, J. "Prediction of the Seakceping Characteristics of Ships," 20th Symposium on Naval Hydrodynamics, Santa Barbara, CA,1994:27-47
[112]Haddara, M., Wishahy, M., Wu, X. Assessment of Ship's Transverse Stability at Sea. Ocean Engineering 21 (8),1994:781-800
[113]吴家驹.随机减量特征矩阵和相关函数矩阵的关系.应用力学学报,1987,4(3)：1-10
[114]Wong H. L. "A Numerical Procedure for Slender Bodies at Leeway," Proc.,3rd Canadian Marine Hydrodynamics and Structures Conference, Halifax/Dartmouth, Nova Scotia, Canada,1995:83-62
[115]Wu Y., Xia J., Du S. "Two Engineering Approaches to Hydro elastic Analysis of Slender Ships," Proc., International Union of Theoretical and Applied Mechanics Memorial Symposium on the Dynamics of Marine Vehicles and Structures in Waves, Brunel University, Uxbridge, U.K., June 1990:24-27,157-165
[116]Cohen, S. B., and Beck, R. F.,1983, "Experimental and Theoretical Hydrodynamic Forces on a Mathematical Model in Confined Waters," J. Ship Res.,27, No.2,75-89
[117]Nahon, M. "A Simplified Dynamics Model for Autonomous Underwater Vehicles," Proc., Symposium on Autonomous Underwater Vehicle Technology, Monterey, CA, June,1996:373-379
[118]Chiu J. K.Random decrement based method for parameter identification of wind-excited building models using acceleration responses.PHD thesis of Colorado State University, USA:Department of Civil Engineering,2004
[119]戴先中.多变量非线性系统的神经网络逆控制方法.科学出版社,2005：43-52
[120]戴先中,刘军,冯纯伯.连续非线性系统的神经网络α阶逆系统控制方法.自动化学报,1998,24(4)：463-468
[121]郑元英.数学分析习题课教程(上册).北京：高等教育出版社,1993
[122]刘用.响应型非线性船舶模型的鲁棒神经网络控制.大连海事大学硕士论文,2004
[123]张智星,孙春在.神经-模糊和软计算[M].西安：西安交通大学出版社,2000：90-111
[124]诸静.模糊控制理论与系统原理.北京：机械工业出版社,2005.8：12-13
[125]Mamdani E. H. Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proc IEEE,1974,121:1585-1588
[126]Faouzi Bonslama, Akira Ichiltawa. Application of limit fuzzy controllers to stability analysis. Fuzzy Sets and Systems,1992,49:103-120
[127]Tsukamoto Y.An approach to fuzzy reasoning method in advance in fuzzy set theory and application. Amsterdam,North-Holland,1997,89:1121-1126
[128]刘向杰,周晓信,柴天佑.模糊控制研究的现状与新发展.信息与控制,1999,28(4)：283-292
[129]Larsen P. M.Industrial application of fuzzy logic control. Int. J.Man Mach Studies, 1980,12(1):3-10
[130]Hirota H,Pedrycz W.Analysis and synthesis of fuzzy systems by the use of fuzzy sets.Fuzzy Sets and Systems,1983,10(1):63-86
[131]杨永鹏,郝燕玲,赵玉新.基于在线密度聚类的潜艇悬停自适应模糊建模[J].华中科技大学学报(自然科学版).2010,38(9)：82-85
[132]林雷,赵紫辉,王洪瑞.基于在线聚类的模糊建模方法及其应用.控制工程,2007,7(14)：22-27
[133]李殿璞.非线性控制系统理论基础.哈尔滨：哈尔滨工程大学出版社,2006：138-149
[134]程代展.非线性系统的几何理论.北京：科学出版社,1988：89-128
[135]陈省身,陈维桓.微分几何讲义.北京：北京大学出版社,1983：143-152
[136]Li Han-Xiong. Quantitative design and analysis of fuzzy proportional-integral-derivative control-a step towards autotuning. International Journal of Systems Science,2000,31 (5):545-553
[137]吴春富.基于遗传算法优化的模糊PID控制研究.控制理论与应用,2005,24(7)：14-18
[138]LI TZUU-HSENG S, Shieh Ming-yuan. Design of a GA based fuzzy PID controller fox non-minimum phase systems.Elsevier Science Publishers B V, Fuzzy Sets and Systems,2001,11(2):183-197
[139]Sousa J. M. et al. Adaptive decision alternatives in fuzzy predictive functions. IEEE Tran. SMC,2001, Part B,31(1):54-56
[140]乔志杰,程翠翠.基于遗传算法优化的模糊PID控制研究及其仿真.皖西学院学报,2009,25(5)：32-109
[141]郭存良.6-DOF并联机器人动力学建模非线性解耦及控制.燕山大学硕士学位论文,2000：10-28
[142]刘金琨.智能控制.北京：电子工业出版社,2005：125-139
[143]楼顺天,胡昌华,张伟编著.基于MATLAB的系统分析与设计：模糊系统.西安：西安电子科技大学出版社,2001,5：56-89
[144]Junku Yuh. A Neural Net Controller for Underwater Robotic Vehicles. IEEE Journal of Oceanic Engineering,1990,15(3):161-166
[145]Chujen Lin, Roger XU, Chiman Kwan, et al. Submarine Pitch and depth control using FCMAC neural networks. Proceedings of the American control conference, Philadelphia, Pennsylvania, Junel998:379-383
[146]贺远华,方彦军.遗传算法在模糊控制器参数寻优中的应用研究.电力自动化设备,2002,22(12)：14-16
[147]高俊吉,黄昆仑,朱军.潜艇定深运动的自适应模糊控制研究.海军工程大学学报,2004,16(2)：83-88
[148]焦李成.神经网络系统理论.西安：西安电子科技大学出版社,1990
[149]Kohonen T. K. Self-organizing and associate memory, Springer-Verlag,1989
[150]Kosko B. Neural networks and fuzzy systems, A dynamical system approach to machine intelligence, Prence-Hall, Int., Inc.,1992
[151]Kosko B. Unsupervised learning in noise, IEEE Trans. Neural Networks,1990,1(1): 44-57
[152]Lin C. T., Lee C. S. G. Neural fuzzy systems-A neuro-fuzzy synergism to intelligent systems, Prentice-Hall Int., Inc.,1996
[153]Lin C. T., Lee C. S. G. Neural network based fuzzy logic control and decision system, IEEE Trans. Computer,1991,40(12):1320-1336
[154]Lin C. T. Neural fuzzy control systems with structure and parameter learning, World Scientific Publishing Co. Pte. Ltd.1994
[155]Nie H., Linkens D. A. Fuzzy-Neural control-Principles, algorithm and applications, Prentice-Hall, Englewood Cliffs, NJ,1989
[156]吴方晖.基于神经网络的带遗传算法的模糊控制器.华南理工大学学报(自然科学版),1999,27(1)：91-95
[157]张毅.基于基因算法的多变量模糊控制器设计.控制理论与应用,1996,(8)：409-429
[158]张化光,何西勤著.模糊自适应控制理论及其应用.北京：北京航空航天大学出版社,2002,7：29-49
[159]Yang Yongpeng, Hao Yanling. Adaptive Fuzzy Modeling of Hovering Submarine Based on On-line Clustering.2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2010), Yantai, China,2011:86-90
[160]Yang Yongpeng, Hao Yanling. Adaptive Control with Decoupling Algorithm for the Hovering Submarines. Proceedings of the 2010 IEEE International Conference on Information and Automation, Harbin, China,June 2010:1382-1386
[2]潘国良.潜艇水下悬停操纵原理研究.研究报告,707研究所九江分部,2004
[3]船舶水动力学词典[M].中国造船编辑部译.上海：中国造舟船编辑部,1981
[4]潘国良.潜艇水下悬停运动的操纵控制研究.东南大学硕士论文.2005
[5]潘国良.潜艇水下悬停及其发展综述.中国造船工程学会2006年船舶通讯导航学术会议,2006：216-221
[6]Richard R J, Stoten D P. Depth Control of a Submersible Vehicles. ISP. 1981,28(318):30-39
[7]孙元泉,马运义等.潜艇和深潜器的现代操纵理论与应用.国防工业出版社,2001
[8]Abkowitz, M.A.,1980. Measurement of Hydrodynamic Characteristics from Ship Maneuvering Trails by System Identification. SNAME Transactions 88:283-318
[9]东昉.刘正江.数字航海.大连：大连海事大学出版社,2010：96-100
[10]Gertler, Hagan. Standard equation of motion for submarine simulation. NSDRC Report 2510,1967
[11]Feidman J. DTNSRDC Revised Standard Submarine Equations of Motion. DTNSRDC Report SPD-0393-09,1979.6
[12]李世保,刘常波,彭达胜等.潜艇六自由度运动仿真器.计算机仿真,1996.1
[13]Drurey L. Automatic control of submarine depth, Pitch and trim. MSEE Thesis, Naval Postgraduate School,Monterey,CA,1975
[14]易炜,迟焕德,张本文.潜艇空间运动线性方程研究.舰船科学技术,2002,6
[15]Joonyoung Kim, Kihun Kim, et aL. Estimation of Hydrodynamic Coefficients for an AUV Using Nonlinear Observers. IEEE Journal of oceanic Engineering,2002, 27(4):830-840
[16]M. M. A. Pourzanjani, H. K. Zienkiewicz, J. O. Flower. A Hybrid of Estimating Hydrodynamically-Generated Forces for Use in Ship-Maneuvering Simulation. ISP, 1987,34:207-216
[17]Feldman J. Straightline and rotating arm captive-model experiments to investigate the stability and control characteristics of submarines and other submerged vehicles. AD-A209575,1987
[18]Feldman J. Method of performing captive-model experiments to predict the stability and control characteristics of submarines. AD-A297814,1995
[19]Lewandouski E M.A new apparatus for captive model testing of submerged bodies in conig motion.23rd ATTC,1993:295-299
[20]Mackay M. Submarine turning circle experiments in a towing tank. International Symposium on Naval submarine,1996
[21]Brownell W F. A rotating arm and maneuvering basin. DTMB Report 1053,1959
[22]Watt G D, et al.Submarine hydrodynamics in the wind tunnel:the DREA static test rig. Warship'91 International Symposium on Naval Submarines,1996
[23]Roddv R, et al.Conceptual design of a new planar motion mechanism for investigating the stability and control characteristics of submarines. AD-A297800, 1995
[24]N S Y. Hydrodynamic forces on a moving submerged body in waves. International Shipbuilding Progress,1994,41:426
[25]Wardle A B. Prediction of normal force, pitching moment and yawing force on bodies of revolution at angles of attack up to 50 degrees using a concentrated vortex flow-field model. AD-A775894,1973
[26]Boudet L. Low frequency vertical forces action on a submerged body:A comparison between theory and experiments. Hydrodynamics Computations model tests and reality,1992:315-320
[27]Cotma A, Broudin J, Renhas Y H. Le nouveau modele libre de sous-matin du Bassin d'Essais des Carenes. Bulletin de I'ATMA,1990,(90):703-733
[28]Renhas Y H, Lempereur C, Mathe J M. Les essays de modele libre de sous-marins en basin de traction:un nouvreau outil pour I'tude hydronamique du sous-marim. Bulletin de I'ATMA,1993,(93)
[29]David Taylor Research Center. Straightline and rotating arm captive-model experiments to investigate the stability and control characteristics of submarines and other submerged vehicles. AD-A2095750XSP,1987,11
[30]Giovanni Indiveri. Modeling and Identification of Underwater Robotic Systems. PHD. Thesis, University of Genova, Genova, Italy, December 1998
[31]D. E. Leader. Kalman Filter Estimation of Underwater Vehicle Position and Attitude Using a Doppler Velocity Aided Inertial Motion Unit. PHD. Thesis, MIT,1994
[32]H. J. Bohlmal.An Analytical Method for the Prediction of submarine Maneuverability. Warship'91 International Symposiumon Naval Submannes,1991
[33]Lioyd A. Progress towards a Rational Method of Predicting Submarine Maneuvers. The Proeeedings of the RINA Iniernational Symposium,1983
[34]W. faller, D. Hess, et al. Applications of recursive neural network technologies to hydrodynamics.22th symposium on Naval Hydrodynamics,2000:705-723
[35]Isherwood R M. Wind resistance of merchant ship Trans.of RINA. No.115,1973
[36]杨盐生.定长风下舵力保向舵角的计算.大连海运学院学报,13(4),1987
[37]Barr R. A. A review and comparison of ship maneuvering methods. SNAME transaction,1993
[38]戴余良,刘祖源,俞科云.潜艇应急上浮操纵与控制研究评述.舰船科学技术,2007,12,15
[39]Bakakin A V, et al. Control algorithms for VSSs:in VSSs and their use in flight automation proc. Nauka, Moscow,1968
[40]Emelyanov S V. Variable structure control system. Nauka, Moscow,1967
[41]Utikin V I. Sliding mode and their application in VSSs. Moscow,1978
[42]Itkis U. Control systems of variable structure. John Wiley&Sons,1976
[43]冯俊梅,连琏,葛彤.潜艇操纵控制方法的现状与发展.海洋工程,2005,23(1)：114-122
[44]Cristi R, Papoulias F A, Healey A J. Adaptive sliding mode control of autonomous underwater vehicles in the dive plane. IEEE, Oceanic Eng,1990,15(3):245-250
[45]赵国良,杨玉红.潜艇垂直面机动的多变量控制.中国造船,1989,(4)：107-116
[46]牟军,许汉珍.潜艇操纵运动的变结构控制.华中理工大学学报,1997,6
[47]牟军.潜艇操纵运动的变结构控制系统研究.华中理工大学博士学论文,1997
[48]金鸿章,王科俊,梁超.具有横摇补偿控制的潜艇航向变结构控制.中国造船,2001
[49]杨惠民.国外核潜艇技术综述.潜艇,1986(1)：115-121
[50]R. K. Lea, Allen S. L. Merry. A comparative study of control techniques for an underwater flight vehicle. Int. J. System Science,1990(9):947-964
[51]H.R.叶费姆也夫.潜艇理论基础.北京：国防工业出版社,1975
[52]施生达.潜艇原理.武汉：海军工程大学出版社,1990
[53]潘国良.舰船操纵控制技术应用与分析.声学与电子工程,2005(增刊)：6-9
[54]何卫华等.潜艇自动操纵控制系统的基本原理及其现状.舰船科学技术,2005,27(3)：20-24
[55]W. F. Klepsen, et al. A study of the impact of SSN 688 Class Submarine Design on a Generalized Advanced Casualty ship Control Training Device.(AD764096), Jun. 1973
[56]Richard A. Stankey. "Trident Ship Control"System Development Program. (AD/AO45603),1975
[57]Michael G. Daughan. Seawolf Submarine Ship Control System:A Case Study of a Fault-Tolerant Design. Naval Engineers Journal, Feb.1994
[58]AD-272913.弹道导弹潜艇的先进船控操纵台.美国通用动力公司,1961
[59]P.巴里诶.潜艇水下悬停时自动定深装置的设计与制造.法国航海与航空技术协会(?)ATMA),1970, NO.70:327-345
[60]王淑英.潜艇操纵控制系统自动化发展研究.“十五”科研发展计划参考资料,中国舰船研究院.1999
[61]施生达.关于潜艇操纵及其自动化技术的近期发展问题探讨.海军工程大学,2001
[62]陈训铨.潜艇操纵自动化的现状及发展.舰船操纵自动化技术专题文,2001
[63]王学峰.常规潜艇水下悬停操纵及战术思考.潜艇42支队,2001
[64]王科俊,姚绪梁,金鸿章.海洋运动体控制原理.哈尔滨工程大学出版社,2005：260-272
[65]吴秀恒,刘祖源,施生达等.船舶操纵性.北京：国防工业出版社,2005
[66]李殿璞.船舶运动与建模.哈尔滨工程大学出版社,1999：102-131
[67]Ahmad S M, Sutton R. Linear, Nonlinear Modeling and AUV retrieval. Plymouth: The University of Plymouth,2000
[68]常文君,刘建成,于华南,徐玉如.水下机器人运动控制与仿真的数学模型.船舶工程.2002,3：58-60
[69]毛斌宏.潜艇空间运动模型及均衡控制技术的研究.哈尔滨工程大学硕士论文,20019
[70]王晓东.潜艇潜浮系统仿真研究初探.舰船科学技术,2004,26(1)：14-19
[71]戴余良.潜艇应急上浮操纵运动分析与控制技术研究.武汉：武汉理工大学博士学位论文,2007.5：14-20
[72]季鲁慧.潜艇运动多变量控制系统研究..哈尔滨船舶工程学院硕士论文,1989：66-70
[73]郝英泽,何斌.海水密度变化对潜艇悬停垂向运动的影响仿真研究.船海工程,第37卷,第2期,2008,4：117-119
[74]齐霄强.潜器悬浮运动模型及控制方法研究.哈尔滨工程大学硕士论文,2008,2
[75]缪泉明,冯学知,孙伯起.圆柱体在近水面的波浪力计算.中国船舶科学研究中心科技报告,1992
[76]缪国平,刘应中,糜振星.斜浪中二维潜体的二阶定常力.中国造船,1994,(125)：20-31
[77]缪国平,刘应中,糜振星.二阶波浪力的切片理论与潜体上的垂向定常力.水动力学 研究与进展,1993,8
[78]缪国平,刘应中,王海平.细长体上的二阶波浪力的切片理论.自然科学进展,1996,5
[79]冯学知,孙伯起,缪泉明.潜艇近水面波浪扰动力研究.中国船舶科学研究中心科技报告,1996
[80]Lee C M, Newman J N. First-and second-order wave effects on a submerged spheroid. J.S.R.1991,35(3):183-19OP
[81]AD-A1429281
[82]王鹢,王文武,孙枫,王国夫.于扰力作用下潜艇近水面运动的仿真.系统仿真学报,2003,15(1)：84-87
[83]邱大洪.波浪理论及其在工程上的应用.北京：高等教育出版社,1985：74-90
[84]Slavesen N. Second-order steady-state forces and moments on surface ship oblique regular wave. Int. Symp. On the Dynamics of the Marine Vehicles and structures in waves. London,1974:212-216
[85]Willmott P. On the motion of a slender body submerged beneath surface wave. J.S.R.1988,32(3)
[86]O'Dea J F, Barr R A. Prediction of wave-induced first order motions and second order forces and moments during submarine maneuvers. Technical Report 7611-1, Hydronautics, Inc,1976,9
[87]Crook T P. An initial assessment of free surface effects on submerged bodies. Master's Thesis, Naval Postgraduate School,1994,9
[88]闵耀元,闫学琳.波浪中潜艇近水而操纵控制规律.719所科技报告,2000
[89]金鸿章.长峰波随机海浪的实时仿真和频谱分析.船舶工程,1987,2(1)：24-30
[90]冯学知,蒋强强,缪泉明,匡晓峰.潜体波浪中近水面不同潜深和航向时运动和波浪力计算.船舶力学,2002,6(2)：1-14
[91]贾欣乐,杨盐生.船舶运动数学模型-机理建模及辨识建模.大连：大连海事大学出版社,1999
[92]COLE H. A. On-the-line analysis of random vibration. AIAA/ASME 9th Structure, Structural Dynamics and Materials Conference,1968
[93]COL E H. A. Method and apparatus for measuring the damping characteristic of a structure. United State:3,620,069,1971
[94]Haddara, M.R., and Xu, J. On the Use of Random Decrement in the Identification of Two Degrees of Freedom Systems, Proceedings of CSME FORUM. Ryerson Polytechnic University. Toronto, May 19-22, vol.4,1998:499-507
[95]陈德成,姜节胜.随机减量技术的方法与理论.振动与冲击,1984,3(4)：31-39
[96]Haddara, M.R. On the Random Decrement for Nonlinear Rolling Motion. Proceedings of the 12th International Conference on Offshore Mechanics and Arctic Engineering, Calgary, Canada,1992:283-288
[97]Mahfouz, A.B. Identification of the Hydrodynamic Model of an Underwater Robotic Vehicle Heaving and Pitching Near the Sea Surface Using its Measured Response, Ph.D. Dissertation, Faculty of Engineering and Applied Science, Memorial University of Newfoundland,2001
[98]R. E. Reed. Analytical Aspects of Randomdec Analysis. AIAA.20,1979
[99]Haddara, M., and Wang, Y. Parametric Identification of Coupled Sway and Yaw Motions, International Conference on Offshore Mechanics and Arctic Engineering, vol.1, Offshore Technology,1996:267-273
[100]Mahfouz, A.B., Haddara, M.R., Williams, C.D. Prediction of the Hydrodynamic Parameters in the Coupled Heave-Pitch Motion Equations for Underwater Robotic Vehicles Using Measured Response at Sea. Journal of Offshore Mechanics and Arctic Engineering 2001,(12)3:1-10
[101]Montgomery D. Design and Analysis of Experiments. New York:John Wiley&Sons,1997
[102]Wang, Y. Ship Maneuverability Prediction Using Neural Networks, M. Eng. thesis, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, 1996
[103]李岳锋.随机减量技术原理探讨.南京航空学院学报,No.2,1984
[104]Yuh, J. "Development in Underwater Robotics," Proc.,IEEE International Conference on Robotics and Automation, Piscataway, NJ,1995:1862-1867
[105]Yuh J. "Control of Underwater Robotic Vehicles," Proc., International Conference on Intelligent Robots and Systems, Yokohama, Japan, July,1993:517-521
[106]张景绘,唐照千.对随机减量技术的数学表示.应用力学学报,VOL.1,No.2,1984
[107]Conte, G, Serrani, A. "Modeling and Simulation of Underwater Vehicles," Proc., IEEE International Symposium on Computer-Aided Control System Design, Dearborn, MI,1996:62-67
[108]Lloyd, A. R. J. M. SEAKEEPING:Ship Behavior in Rough Weather, Ellis Harwood Limited,1989
[109]Bhattacharyya, R. Dynamics of Marine Vehicles, John Wiley & Sons Inc., New York, NY,1978
[110]胡耀华,贾欣乐.具有约束条件的船舶运动预测控制.控制理论与应用, 2000,17(4):452-457
[111]Bingham, H., Korsmeyer, F., Newman, J. "Prediction of the Seakceping Characteristics of Ships," 20th Symposium on Naval Hydrodynamics, Santa Barbara, CA,1994:27-47
[112]Haddara, M., Wishahy, M., Wu, X. Assessment of Ship's Transverse Stability at Sea. Ocean Engineering 21 (8),1994:781-800
[113]吴家驹.随机减量特征矩阵和相关函数矩阵的关系.应用力学学报,1987,4(3)：1-10
[114]Wong H. L. "A Numerical Procedure for Slender Bodies at Leeway," Proc.,3rd Canadian Marine Hydrodynamics and Structures Conference, Halifax/Dartmouth, Nova Scotia, Canada,1995:83-62
[115]Wu Y., Xia J., Du S. "Two Engineering Approaches to Hydro elastic Analysis of Slender Ships," Proc., International Union of Theoretical and Applied Mechanics Memorial Symposium on the Dynamics of Marine Vehicles and Structures in Waves, Brunel University, Uxbridge, U.K., June 1990:24-27,157-165
[116]Cohen, S. B., and Beck, R. F.,1983, "Experimental and Theoretical Hydrodynamic Forces on a Mathematical Model in Confined Waters," J. Ship Res.,27, No.2,75-89
[117]Nahon, M. "A Simplified Dynamics Model for Autonomous Underwater Vehicles," Proc., Symposium on Autonomous Underwater Vehicle Technology, Monterey, CA, June,1996:373-379
[118]Chiu J. K.Random decrement based method for parameter identification of wind-excited building models using acceleration responses.PHD thesis of Colorado State University, USA:Department of Civil Engineering,2004
[119]戴先中.多变量非线性系统的神经网络逆控制方法.科学出版社,2005：43-52
[120]戴先中,刘军,冯纯伯.连续非线性系统的神经网络α阶逆系统控制方法.自动化学报,1998,24(4)：463-468
[121]郑元英.数学分析习题课教程(上册).北京：高等教育出版社,1993
[122]刘用.响应型非线性船舶模型的鲁棒神经网络控制.大连海事大学硕士论文,2004
[123]张智星,孙春在.神经-模糊和软计算[M].西安：西安交通大学出版社,2000：90-111
[124]诸静.模糊控制理论与系统原理.北京：机械工业出版社,2005.8：12-13
[125]Mamdani E. H. Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proc IEEE,1974,121:1585-1588
[126]Faouzi Bonslama, Akira Ichiltawa. Application of limit fuzzy controllers to stability analysis. Fuzzy Sets and Systems,1992,49:103-120
[127]Tsukamoto Y.An approach to fuzzy reasoning method in advance in fuzzy set theory and application. Amsterdam,North-Holland,1997,89:1121-1126
[128]刘向杰,周晓信,柴天佑.模糊控制研究的现状与新发展.信息与控制,1999,28(4)：283-292
[129]Larsen P. M.Industrial application of fuzzy logic control. Int. J.Man Mach Studies, 1980,12(1):3-10
[130]Hirota H,Pedrycz W.Analysis and synthesis of fuzzy systems by the use of fuzzy sets.Fuzzy Sets and Systems,1983,10(1):63-86
[131]杨永鹏,郝燕玲,赵玉新.基于在线密度聚类的潜艇悬停自适应模糊建模[J].华中科技大学学报(自然科学版).2010,38(9)：82-85
[132]林雷,赵紫辉,王洪瑞.基于在线聚类的模糊建模方法及其应用.控制工程,2007,7(14)：22-27
[133]李殿璞.非线性控制系统理论基础.哈尔滨：哈尔滨工程大学出版社,2006：138-149
[134]程代展.非线性系统的几何理论.北京：科学出版社,1988：89-128
[135]陈省身,陈维桓.微分几何讲义.北京：北京大学出版社,1983：143-152
[136]Li Han-Xiong. Quantitative design and analysis of fuzzy proportional-integral-derivative control-a step towards autotuning. International Journal of Systems Science,2000,31 (5):545-553
[137]吴春富.基于遗传算法优化的模糊PID控制研究.控制理论与应用,2005,24(7)：14-18
[138]LI TZUU-HSENG S, Shieh Ming-yuan. Design of a GA based fuzzy PID controller fox non-minimum phase systems.Elsevier Science Publishers B V, Fuzzy Sets and Systems,2001,11(2):183-197
[139]Sousa J. M. et al. Adaptive decision alternatives in fuzzy predictive functions. IEEE Tran. SMC,2001, Part B,31(1):54-56
[140]乔志杰,程翠翠.基于遗传算法优化的模糊PID控制研究及其仿真.皖西学院学报,2009,25(5)：32-109
[141]郭存良.6-DOF并联机器人动力学建模非线性解耦及控制.燕山大学硕士学位论文,2000：10-28
[142]刘金琨.智能控制.北京：电子工业出版社,2005：125-139
[143]楼顺天,胡昌华,张伟编著.基于MATLAB的系统分析与设计：模糊系统.西安：西安电子科技大学出版社,2001,5：56-89
[144]Junku Yuh. A Neural Net Controller for Underwater Robotic Vehicles. IEEE Journal of Oceanic Engineering,1990,15(3):161-166
[145]Chujen Lin, Roger XU, Chiman Kwan, et al. Submarine Pitch and depth control using FCMAC neural networks. Proceedings of the American control conference, Philadelphia, Pennsylvania, Junel998:379-383
[146]贺远华,方彦军.遗传算法在模糊控制器参数寻优中的应用研究.电力自动化设备,2002,22(12)：14-16
[147]高俊吉,黄昆仑,朱军.潜艇定深运动的自适应模糊控制研究.海军工程大学学报,2004,16(2)：83-88
[148]焦李成.神经网络系统理论.西安：西安电子科技大学出版社,1990
[149]Kohonen T. K. Self-organizing and associate memory, Springer-Verlag,1989
[150]Kosko B. Neural networks and fuzzy systems, A dynamical system approach to machine intelligence, Prence-Hall, Int., Inc.,1992
[151]Kosko B. Unsupervised learning in noise, IEEE Trans. Neural Networks,1990,1(1): 44-57
[152]Lin C. T., Lee C. S. G. Neural fuzzy systems-A neuro-fuzzy synergism to intelligent systems, Prentice-Hall Int., Inc.,1996
[153]Lin C. T., Lee C. S. G. Neural network based fuzzy logic control and decision system, IEEE Trans. Computer,1991,40(12):1320-1336
[154]Lin C. T. Neural fuzzy control systems with structure and parameter learning, World Scientific Publishing Co. Pte. Ltd.1994
[155]Nie H., Linkens D. A. Fuzzy-Neural control-Principles, algorithm and applications, Prentice-Hall, Englewood Cliffs, NJ,1989
[156]吴方晖.基于神经网络的带遗传算法的模糊控制器.华南理工大学学报(自然科学版),1999,27(1)：91-95
[157]张毅.基于基因算法的多变量模糊控制器设计.控制理论与应用,1996,(8)：409-429
[158]张化光,何西勤著.模糊自适应控制理论及其应用.北京：北京航空航天大学出版社,2002,7：29-49
[159]Yang Yongpeng, Hao Yanling. Adaptive Fuzzy Modeling of Hovering Submarine Based on On-line Clustering.2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2010), Yantai, China,2011:86-90
[160]Yang Yongpeng, Hao Yanling. Adaptive Control with Decoupling Algorithm for the Hovering Submarines. Proceedings of the 2010 IEEE International Conference on Information and Automation, Harbin, China,June 2010:1382-1386