船舶液货装卸智能控制系统关键技术研究
|
摘要
随着科学技术的进步和船舶运输业的蓬勃发展,为了提高液货装卸效率,缩短船舶在港停留时间,最终达到提高经济效益这一根本目的,作为液货船重要的组成部分之一的液货装卸控制系统越来越趋于复杂化、智能化,成为融合计算机、自动控制和电子信息技术,以及船舶理论等多学科的综合系统。目前的液货装卸系统由许多保护子系统和安全子系统构成,如阀门遥控系统、惰气保护系统、液位测量系统,高位及高高位报警系统等,这些系统存在时滞、时变等非线性问题,如何解决这些问题并将所有子系统融合到一起实现智能控制是个技术难题。在我国,大型高性能液货船舶的装卸系统一般都是直接引进国外的技术,这就造成以下几个问题:(1)液货装卸系统可以创造巨大的经济效益,国外采用技术限制使我们无法知道内部具体的算法实现;(2)虽然国外的液货装卸控制软件具有工程优化及先进控制等功能,但参数调节较为复杂;(3)直接引入国外的技术,使我们缺乏自主开发的工程软件,即使国内学者提出了好的控制算法,也无法找到实现平台。因此,研究液货装卸智能控制系统实现的关键技术,无论在理论和实际应用都有十分重要的意义。
本文在掌握国内外液货装卸智能控制系统研究现状的基础上,对系统的关键技术展开了进一步的研究工作,主要研究内容包括:
(1)船舶液货装卸系统中的许多信号是带噪声的非平稳非线性信号,本文重点研究了适用于非平稳、非线性信号处理的经验模态分解算法(Empirical Mode Decomposition,EMD),为了提高局部EMD算法的分解精度,对局部EMD方法进行了改进。在第一次EMD分解时不区分极大极小值,先对其组成的序列直接用三点滑动平均的方法求均值,然后采用B样条对数据序列进行插值拟合,其余的分解过程采用三次样条拟合极值包络,这样B样条函数可以组成样条空间的基底,避免了三次样条在数据高频部分分解过程中产生的过冲和欠冲现象。
(2)针对含有间断点的信号在进行EMD分解时产生的模式混叠现象进行了研究,在采用小波去噪方法对信号进行预处理时,为了消除小波去噪时在间断点附近产生的伪Gibbs现象,提出了先采用平移不变算法对信号进行处理,然后进行小波阈值去噪,再将去噪后的数据进行EMD分解的方法。采用这种方法可有效地消除间断点引起的模式混叠现象。
(3)液货装卸控制系统中有些子系统的数学模型很难建立,为了实现系统的智能控制,本文深入研究了无模型控制方法,该方法是一类兼有现代控制理论与经典PID优点的方法,仅采用系统的输入、输出数据设计控制器,实现不依赖被控系统数学模型。进而提出了基于多新息理论的伪梯度估计无模型控制方法和基于多新息理论的无模型控制律,充分利用系统以前的输入输出数据来提高无模型控制方法的收敛速度,并且给出了所提方法的理论推导和收敛性证明。最后采用粒子群算法和人工鱼群算法分别对所提两种方法的参数优化进行了研究。
(4)面向工程实际,提出了基于开放式控制系统开发规范的液货装卸智能控制系统的框架结构,搭建了液货装卸智能控制系统的软硬件平台。开放式体系结构所具有的层次化、模块化和构件化的设计方式使该平台具有了透明性好、可靠性高、伸缩性大和组态性强等特点,用户可以按照需要加入自己的算法,而不用考虑底层硬件实现。基于该框架结构实现了一条16000DWT成品油船的液货装卸智能控制系统,并且进一步实现了12000T浮船坞实时配载系统和5000T半潜驳实时配载系统,表明所建框架平台具有一定的可拓展性。最后将本文所提改进局部EMD方法和改进无模型控制方法分别应用到液货装卸系统的舱室液位信号处理和舱室压力、流量去耦控制,结果表明该平台能够为研究装卸系统的信号处理算法和控制算法提供良好的实现环境。
In order to improve handling efficiency of tankers,reduce the time of the ship settling in port and increase economic benefit,the liquid cargo handling system(LCHS) tends to complication and intelligence with the development of many subjects like computer, automatic control,signal process and shipping transport service,which is consist of many subsystems such as loading/unloading system,inert gas system,tank depth measurement system,high/high-level alarming system etc.How to integrate these subsystems which have time-delay or time-varying is difficult problem.The liquid cargo handling systems of high performance ships in out country depend on directly importing foreign advanced technology,which leads to some questions:(1) The foreign LCHS is a technology black box for us due to existing economic interests.(2) The tuning of parameters is very complex in foreign LCHS although it has advanced process control and optimization techniques.(3) there is a lack of self-developed engineering LCHS because of directly importing LCHS. So it is very important in theory and practice that research on the key technologies of LCHS.
This paper is on the basis of the domestic and foreign research status of LCHS, further the research work in this domain.And the main contents are as follows:
(1) Aiming at having the nonlinear and non-stationary property of signals in LCHS, we mainly studied the Empirical Mode Decomposition(EMD) algorithm which was suitable for nonlinear and non-stationary signal.In order to improve the precision of local-EMD, when to get the first Intrinsic Mode Function(IMF),we firstly adopted three points sliding average method to get the mean of data envelopment which did not distinguish extremum,then used the non-uniform B-spline curve to interpolate data.Other IMFs were gotten by using cubic spline to fit maximum envelope and minimum envelope respectively. The improved method could avoid the overshoot and undershoot in high-frequency decomposition of signal data.
(2)One method was proposed to solve the mode mix problem.The signal data was retreated with wavelet transform,because pseudo-Gibbs caused by wavelet transform still brought mode mix problem,so we adopted the de-noising method based on translations invariant performed the cycle spinning for the signal to be analyzed at first.Secondly,the soft(hard) thresholding was used to de-noise the sfifted signal,then one should shift the data of the de-noised signal in reverse.At last,the treated signal data was decomposed by EMD.
(3)Many mathematical models of subsystems of LCHS could not be suitably given, so it is difficult to control the subsystems by using modern control theory.We studied the model free control algorithm which is a new control algorithm and combines modern theories with classical PID,and then we proposed the model free control of pseudo gradient estimation algorithm based on multi-innovation theory and the model free control method of model free control law based on multi-innovation theory,which improved the convergence performance of model free control algorithm by making full use of the past input/output data.The theoretical derivation and convergence proof of two methods were given,which showed the proposed methods were efficient and effective.At last,we studied the parameters optimization of the two methods based on Particle Swarm Optimization (PSO) and Artificial Fish Swarm(AFS).
(4)Faced to engineering practice,a framework of LCHS based on open control system structure was proposed,in which the designer can add new algorithms according to specific problems but not considering the hardware.A practical LCHS of 16000DWT tanker was constructed.Based on the implement system,we researched on the signal process of liquid level by using proposed local-MED algorithm in the paper and the control effect of the proposed multi-innovation model free control method in pressure and flow of liquid cabin.And we completed a real-time stowage system of 12000T floating dock and a real-time stowage system of 5000T semi-submersible barge,which could show that the proposed framework in the paper has engineering value.
本文在掌握国内外液货装卸智能控制系统研究现状的基础上,对系统的关键技术展开了进一步的研究工作,主要研究内容包括:
(1)船舶液货装卸系统中的许多信号是带噪声的非平稳非线性信号,本文重点研究了适用于非平稳、非线性信号处理的经验模态分解算法(Empirical Mode Decomposition,EMD),为了提高局部EMD算法的分解精度,对局部EMD方法进行了改进。在第一次EMD分解时不区分极大极小值,先对其组成的序列直接用三点滑动平均的方法求均值,然后采用B样条对数据序列进行插值拟合,其余的分解过程采用三次样条拟合极值包络,这样B样条函数可以组成样条空间的基底,避免了三次样条在数据高频部分分解过程中产生的过冲和欠冲现象。
(2)针对含有间断点的信号在进行EMD分解时产生的模式混叠现象进行了研究,在采用小波去噪方法对信号进行预处理时,为了消除小波去噪时在间断点附近产生的伪Gibbs现象,提出了先采用平移不变算法对信号进行处理,然后进行小波阈值去噪,再将去噪后的数据进行EMD分解的方法。采用这种方法可有效地消除间断点引起的模式混叠现象。
(3)液货装卸控制系统中有些子系统的数学模型很难建立,为了实现系统的智能控制,本文深入研究了无模型控制方法,该方法是一类兼有现代控制理论与经典PID优点的方法,仅采用系统的输入、输出数据设计控制器,实现不依赖被控系统数学模型。进而提出了基于多新息理论的伪梯度估计无模型控制方法和基于多新息理论的无模型控制律,充分利用系统以前的输入输出数据来提高无模型控制方法的收敛速度,并且给出了所提方法的理论推导和收敛性证明。最后采用粒子群算法和人工鱼群算法分别对所提两种方法的参数优化进行了研究。
(4)面向工程实际,提出了基于开放式控制系统开发规范的液货装卸智能控制系统的框架结构,搭建了液货装卸智能控制系统的软硬件平台。开放式体系结构所具有的层次化、模块化和构件化的设计方式使该平台具有了透明性好、可靠性高、伸缩性大和组态性强等特点,用户可以按照需要加入自己的算法,而不用考虑底层硬件实现。基于该框架结构实现了一条16000DWT成品油船的液货装卸智能控制系统,并且进一步实现了12000T浮船坞实时配载系统和5000T半潜驳实时配载系统,表明所建框架平台具有一定的可拓展性。最后将本文所提改进局部EMD方法和改进无模型控制方法分别应用到液货装卸系统的舱室液位信号处理和舱室压力、流量去耦控制,结果表明该平台能够为研究装卸系统的信号处理算法和控制算法提供良好的实现环境。
In order to improve handling efficiency of tankers,reduce the time of the ship settling in port and increase economic benefit,the liquid cargo handling system(LCHS) tends to complication and intelligence with the development of many subjects like computer, automatic control,signal process and shipping transport service,which is consist of many subsystems such as loading/unloading system,inert gas system,tank depth measurement system,high/high-level alarming system etc.How to integrate these subsystems which have time-delay or time-varying is difficult problem.The liquid cargo handling systems of high performance ships in out country depend on directly importing foreign advanced technology,which leads to some questions:(1) The foreign LCHS is a technology black box for us due to existing economic interests.(2) The tuning of parameters is very complex in foreign LCHS although it has advanced process control and optimization techniques.(3) there is a lack of self-developed engineering LCHS because of directly importing LCHS. So it is very important in theory and practice that research on the key technologies of LCHS.
This paper is on the basis of the domestic and foreign research status of LCHS, further the research work in this domain.And the main contents are as follows:
(1) Aiming at having the nonlinear and non-stationary property of signals in LCHS, we mainly studied the Empirical Mode Decomposition(EMD) algorithm which was suitable for nonlinear and non-stationary signal.In order to improve the precision of local-EMD, when to get the first Intrinsic Mode Function(IMF),we firstly adopted three points sliding average method to get the mean of data envelopment which did not distinguish extremum,then used the non-uniform B-spline curve to interpolate data.Other IMFs were gotten by using cubic spline to fit maximum envelope and minimum envelope respectively. The improved method could avoid the overshoot and undershoot in high-frequency decomposition of signal data.
(2)One method was proposed to solve the mode mix problem.The signal data was retreated with wavelet transform,because pseudo-Gibbs caused by wavelet transform still brought mode mix problem,so we adopted the de-noising method based on translations invariant performed the cycle spinning for the signal to be analyzed at first.Secondly,the soft(hard) thresholding was used to de-noise the sfifted signal,then one should shift the data of the de-noised signal in reverse.At last,the treated signal data was decomposed by EMD.
(3)Many mathematical models of subsystems of LCHS could not be suitably given, so it is difficult to control the subsystems by using modern control theory.We studied the model free control algorithm which is a new control algorithm and combines modern theories with classical PID,and then we proposed the model free control of pseudo gradient estimation algorithm based on multi-innovation theory and the model free control method of model free control law based on multi-innovation theory,which improved the convergence performance of model free control algorithm by making full use of the past input/output data.The theoretical derivation and convergence proof of two methods were given,which showed the proposed methods were efficient and effective.At last,we studied the parameters optimization of the two methods based on Particle Swarm Optimization (PSO) and Artificial Fish Swarm(AFS).
(4)Faced to engineering practice,a framework of LCHS based on open control system structure was proposed,in which the designer can add new algorithms according to specific problems but not considering the hardware.A practical LCHS of 16000DWT tanker was constructed.Based on the implement system,we researched on the signal process of liquid level by using proposed local-MED algorithm in the paper and the control effect of the proposed multi-innovation model free control method in pressure and flow of liquid cabin.And we completed a real-time stowage system of 12000T floating dock and a real-time stowage system of 5000T semi-submersible barge,which could show that the proposed framework in the paper has engineering value.
引文
[1]黄忠秀.船舶运输控制系统.北京:人民交通出版社,2000.
[2]川原靖夫.智能化船的液货装卸系统自动化.船舶,1991,6:37-39.
[3]Wiener N.Extrapolation,Interpolation,and Smoothing of Stationary Time Series,with Engineering Application.New York:Tech.Press of M.I.T.and John Wiley and Sons,1949.
[4]Wiener N.Cybernetics or Control and Communication in the Animal and the Machine.New York:Tech.Press of M.I.T.and John Wiley and Sons,1961.
[5]张有为.维纳与卡尔曼滤波理论导论.北京:人民教育出版社,1980.
[6]王宏禹,邱天爽.自适应噪声抵消与时间延迟估计.大连:大连理工大学出版社,1999.
[7]Carter G C,etal.Estimation of magnitude-sguare coherence function via overlapped fast fourier transform processing.IEEE Trans.Audio and Electroacoustics,1973,21(4):337-344.
[8]Bendat J S,Piersol A G.Engineering apphcation of correlation and spectral analysis,2nd Edition.Willey,USA,1993.
[9]李启虎.自适应噪声抵消滤波器抵消能力的研究.电子学报,1981,4:16-24.
[10]韩新洁,孟宪尧.船舶液位测量中海浪的滤波处理.大连海事大学学报,1999,25(1):11-14.
[11]Cooley J,Lewis P A.An algorithm for the machine calculation of comples fourier series.Mathematics of Computation,1965,19(90):95-139.
[12]Portnoff M.Time-frequency representation of digital signals and systems based on short-time fourier analysis.IEEE Transactions on Acoustics,Speech,and Signal Processing,1980,28(1):55-69.
[13]Cvetkovic Z.On discrete short-time fourier analysis.IEEE Transactions on Signal Processing,2000,48(9):2628-2640.
[14]谢杰成,张大力,徐立文.小波图象去噪综述.中国图象图形学报,2002,7(3):209-217.
[15]Mallat S.A theory for multiresolution signal decomposition:the wavelet representation.IEEE Transactions on PAMI,1989,11(7):674-693.
[16]Mallat S.Characterization of signals from nultiscale edges.IEEE Transactions on PAMI,1992,14(7):710-732.
[17]彭玉华,汪文秉.小波用于估测散射波波达时间及去噪.电子学报,1996,24(4):113-116.
[18]刘朝山,张维强等.一种改善小波变换模极大值重构信号的整体变分方法.电子学报,2004,32(10):1713-1715.
[19]Donoho D L,Johnstone M.Ideal spatial adaption by wavelet shrinkage.Biometrika,1994,81(3):425-455.
[20]Krim H,Schick C.Minmax description length fro signal denoising and optimized representation.IEEE Trans.Information Theory,1999,45(3):898-908.
[21]Xie J,Zhang D,Xu W.Spatially adaptive wavelet denoising using the minimum description length principle.IEEE Trans.on Image Processing,2004,13(2):179-187.
[22]Sun W,Palazoglu A.Detecting abnormal process trends by wavelet-domain hidden markov models.AICHE Journal,2003,49(1):140-150.
[23]于德介,程军圣.机械故障诊断的Hilbert-Huang变换方法.北京:科学出版社,2007.
[24]Huang N E,Steven Z S,Long R,etal.The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis.Proc.R.Soc.A,1998,454:903-995.
[25]Huang N E,Shen Z,Long S R.A new view of nonlinear water waves:the hilbert spectrum.A.Rev.Fluid Mech.,1999,31:417-457.
[26]Huang N E,Shih H H,Shen Z,etal.The ages of large-amplitude coastal seiches on the caribbean coast of puerto rico.Journal Phys.Oceanogr,2000,30:2001-2012.
[27]王卫强,陈宗镛,左军成.经验模态法在中国沿岸海平面变化中的应用研究.海洋学报,1999,21(6):102-109.
[28]Deng Y,Wang W.Boundary processing technique in emd method and hilbert transform.Chinese Science Bulletin,2001,46(11):257-263.
[29]Rillng G,Flandrin P,etal.On empirical mode decomposition and its algorithms.IEEE EURASIP Workshop on Nonlinear Signal and Image Processing(I),2003,June:9-11.
[30]蒋爱平,李秀英,韩志刚.从pid到无模型控制器.控制工程,2005,12(3):217-220.
[31]Rohrs C E,Valavani L,Athans M,etal.Analytical verification of undesirable properties of direct model reference adaptive control algorithms.Proc.20th IEEE CDC,1981:1272-1284.
[32]Richalet J,Rault A,Testud J L,etal.Model predictive heuristic control:application to industrial processes.Automatica,1976,14:413-428.
[33]Garcia.G E,Morshedi A M.Quadratic programming solution of dynamic matrix control.Chem.Eng.Commun.,1981,46(1):73-87.
[34]Clarke E W,Mohtadi C,Tuffs P S.Generalized preditive control.Automatica,1987,23(2):137-162.
[35]Manfred M,Jay H L.Model predictive control:past,present and future.Computers and Chemical Engineering,1999,23:667-682.
[36]Zadeh L A.Fuzzy sets.Information and Control,1965,8:338-353.
[37]罗熊,孙增圻.计算智能方法优化设计模糊控制系统:现状与展望.控制与决策,2007,22(9):961-966.
[38]Raju G V S.Adaptive hierarchical fuzzy controller.IEEE Trans.on Systems,Man and Cybemetics,1993,23(4):973-980.
[39]Linkens D A,Nie J Constructing rule-based for multivariable fuzzy control by self-learning.Int.J.System Science,1993,24(1):111-127.
[40]Wang L X.Fuzzy systems as universal approximators.IEEE Int.Conf.Fuzzy Systems,San Diego,USA,1992,2(2):162-176.
[41]Castro J L,Pelgado M.Fuzzy systems with defuzzification are universal approximators.IEEE Trans.on Systems,Man and Cybernetics,1996,26(1):149-152.
[42]Sun Z.Controller design and stability analysis of time-continuous systems based on a fuzzy state model.Acta Automatics.Sinica,1998,24(2):212-216.
[43]张恩勤,施颂椒等.模糊控制系统近年来的研究与发展.控制理论与应用,2001,18(1):7-11.
[44]Hunt K J,D S,etal.Neural network for control system:a survey.Automatica,1992,28(6):1083-1112.
[45]Narendra K S.Identifcation and control of dynamic systems using neural networks.IEEE Transactions on Neural Networks,1990,(1):4-27.
[46]黄道平.多变量非线性过程控制—预测、神经网络、解耦控制方法.华南理工大学博士学位论文,1998.
[47]Sorensen P H,Morgaard M,Ravn O,etal.Implementation of neural network based non-linear predictive control.Neurocomputing,1999,28:37-51.
[48]Hunt K J,Sbarbaro D.Neural networks for nonlinear interanl model control.Proc.IEE PID,1991,138(2):431-438.
[49]Chen F C,Khalil H K.Adaptive control of a class of nonlinear discrete-time systems using neural networks.IEEE Trans.on Automatic Control,1995,40(5):791-801.
[50]Jang J S R.Self-learning fuzzy controllers based on temporal back propagation.IEEE Trans.on Neural Network,1992,3(5):723-774.
[51]Barto A G,Sutton R S,Anderson C W.Neuronlike adaptive elements that can solve difficult learning control problems.IEEE Trans.on Systems,Man and Cybernetics,1987,13(5):834-846.
[52]邵世煌,龚道勇,赵杏弟.一个基于“类神经元”模型的智能控制系统及其在柔性臂上的研究.控制与决策,1990,5(2):32-37.
[53]舒怀林.Pid神经元网络控制系统分析.自动化学报,1999,25(1):105-111.
[54]王宁.神经元网络控制理论及应用研究.华中理工大学博士学位论文,1992.
[55]王宁,涂建.电渣重熔过程的神经元智能控制.自动化学报,1993,19(5):634-636.
[56]Cheng G S.Model-free adaptive(mfa) control.IEE Computing and Control Engineering,2004,15(3):28-33.
[57]侯忠生,韩志刚.非线性系统鲁棒无模型学习自适应控制.控制与决策,1995,10(2):137-142.
[58]Arimoto S,Kawamura S,Miyazaki F.Bettering operation of robotics by learning.Journal of Robotic System,1984,1(2):123-140.
[59]Safonov M G,Tsao T C.The unfalsified control concept and leering.IEEE Trans.Atuom.Control,1997,42(6):843-847.
[60]韩志刚.一类无模型系统的控制律及其仿真研究.第一届全球华人智能控制与智能自动化大会论文集,北京:科学出版社,1993:2603-2608.
[61]侯忠生.非线性系统参数辨识、自适应控制和无模型学习自适应控制.东北大学博士论文,1994.
[62]韩志刚,王德进.无模型控制器.黑龙江大学自然科学学报,1994,11(4):29-35.
[63]张铁柱.无模型控制理论及其应用.东北大学博士论文,2002.
[64]韩志刚.无模型控制器的基本形式及其应用效果分析.第三届全球华人智能控制与智能自动化大会论文集,合肥:中国科学技术大学出版社,2000:2902-2906.
[65]韩志刚,蒋爱平,汪国强.无模型控制方法对多变量耦合系统控制的研究.控制与决策,2004,19(10):1155-1158.
[66]王玲,张明波.多步时滞非线性多输入系统的无模型控制律的一种改进算法.黑龙江大学自然科学学报,1997,14(3):31-35.
[67]曹荣敏,侯忠生.无模型控制方法在直线电机控制中的仿真研究.系统仿真学报,2006,18(10):2874-2877.
[68]张铁柱,汪国强.无模型控制器在焦化炉上的应用效果分析.第21届中国控制会议文集,杭州,2002:265-268.
[69]马洁.船舶运动姿态预报与控制方法研究.哈尔滨工程大学博士论文,2006.
[70]韩志刚.无模型控制方法在化肥生产中的应用.控制理论与应用,2004,21(6):858-863.
[71]韩志刚,蒋爱平,汪国强.合成氨生产中的氢氮比控制系统.控制理论与应用,2005,22(5):762-766.
[72]孟宪尧.数据融合技术与船舶自动化.大连:大连海事大学出版社,2003.
[73]Richard B,Christopher W F.Cargo pump performance versus design criteria.Naval Engineers Journal,1998,4:47-60.
[74]Tviberg G.The new generation product tanker and its cargo pumping system.Brodogradnja,2001,49:329-332.
[75]肖民,姚寿广等.船舶压载水系统仿真建模型与控制的研究与实现.系统仿真学报,2005,17(12):3036-3039.
[76]吴宛青.油船货油阀液压系统的建模与数据仿真.大连海事大学学报,1999,25(1):349-38.
[77]齐国清.油船舱内液面高度测量雷达系统.中国造船,1997,137(2):96-100.
[78]徐邦林.液化天然气船舶液货系统的仿真研究.大连海事大学硕士论文,2002.
[79]杨立,林焰,陈明等.成品油船货油装卸系统仿真研究.中国造船,2006,47(2):82-88.
[80]于鹏.LonWorks总线在船舶油舱监控系统中的应用研究.大连海事大学硕士论文,2005.
[81]杨光.现代油船液货自动装卸的仿真控制研究.大连海事大学硕士论文,2005.
[82]Sato K,Itoh H,Awashima.Y.Expert system for oil tanker loading/unloading operation planning.Applications in the Automation of Shipyard Operation and Ship Design,IFIP,1992:82-88.
[83]Villabo M.Liquid,cargo handling training simulator.Norwegian Maritime Research,1984,12:30-35.
[84]Jakobsen B K,Miller E R,Alman P.The u.s.merchant marine academy liquid cargo handling simulator.Marine Technology,1990,27:257-264.
[85]吴宛青,殷佩海.油船货油装卸操作模拟器的系统设计.中国航海,1999,44:71-74.
[86]杨建立,吴宛青.油船货油系统的仿真研究.大连海事大学学报,1999,25(3):63-68.
[87]杨立.液货船货油装卸系统仿真及软件开发.大连理工大学硕士论文,2005.
[88]曾向明.大型油轮货油装卸模拟装置开发研究.上海海事大学硕士论文,2003.
[89]朱仁庆,王志东,方智勇.液舱内大幅晃荡引起的压强预报(英文).船舶力学,2004,8(6):71-78.
[90]程军圣.基于Hilbert-Huang变换的旋转机械故障诊断方法研究.湖南大学博士学位论文,2005.
[91]Mandel L.Interpretation of instantaneous frequency.Amer.J.Phys.,1974,4(2):840-846.
[92]Cohen.Time frequency analysis.Englewood Cliffs,NJ:Prentice Hall,1995.
[93]Boashash B.Estimating and interpreting the instantaneous frequency of a signal.Proc.of IEEE,1992,80(4):520-538.
[94]Long S R,Huang N E,Tung C E,etal.The hilbert techniques:an alternate approach for non-steady time series analysis.IEEE Geoscience Remote Sensing Soc,Let,1995,3:6-11.
[95]熊卫华.经验模态分解方法及其在变压器状态监测中的应用研究.浙江大学博士论文,2006.
[96]Chen Q H,Huang E,etal.A b-spline approach for empirical mode decompositions.Advances in Computational mathematics,2006,(24):171-195.
[97]杨世锡,胡劲松,吴昭同.基于高次样条插值的emd方法研究.浙江大学学报(工学版),2004,38(3):267-270.
[98]Huang D J,Zhao J P,Su J L.Practical implementation of hilbert-huang transform algorithm.Acta Oceanologica Sinica,2003,22(1):1-14.
[99]Rilling G,Flandrin P,P G.On empirical mode decomposition and its algorithms.IEEE EURASIP Workshop on Nonlinear Signal and Image Processing,Grado(Ⅰ),June 9-11,2003.
[100]张郁山,梁建文,胡聿贤.利用自回归模型处理emd方法中的边界问题.自然科学进展,2003,13(10):1054-1059.
[101]刘慧婷,张旻,程家兴.基于多项式拟合算法的emd端点问题的处理.计算机工程与应用,2004,(16).
[102]许宝杰,张建民,徐小力等.抑制emd端点效应方法的研究.北京理工大学学报,2006,26(3):196-200.
[103]谭善文.多分辨Hilbert-Huang变换方法的研究.重庆大学博士论文,2001.
[104]郑天翔,杨力华.经验模式分解算法的探讨和改进.中山大学学报,2007,46(1):1-6.
[105]余泊.自适应时频分析方法及其在故障论断中的应用.大连理工大学博士论文,1998.
[106]盖强.局域波时频分析方法的理论研究与应用.大连理工大学博士论文,2001.
[107]施法中.计算机辅助几何设计与非均匀有理B样条.北京:高等教育出版社,2005.
[108]Liu B,Riemenschneider S,Y X.Gearbox fault diagnosis using empirical mode decomposition and hilbert spectrum.Mechanical Systems and Signal Processing,2006,20:718-734.
[109]申丽然.Hilbert-Huang变换及其在含噪语音信号处理中的应用研究.哈尔滨工程大学博士论文,2006.
[110]赵进平.异常事件对emd方法的影响及其解决方法研究.青岛海洋大学学报,2001,31(6):805-814.
[111]Li H L,Yang L H.The study of the intermittency test filtering character of hilbert huang transform.Matheratics stud computers in simulation,2005,70:22-32.
[112]Donoho D L.De-noising by soft-thresholding.IEEE Transactions on Information Theory,1995,41(3):613-627.
[113]Coifman R R,Donoho D L.Translation-invariant de-noising.Wavelets and Statistics,1995,103:125-150.
[114]汤宝平,杨昌棋,谭善文等.基于平移不变的小波去噪方法及应用.重庆大学学报,2002,25(3):1-5.
[115]侯忠生.无模型自适应控制的现状与展望.控制理论与应用,2006,23(4):586-591.
[116]李伟.无模型控制器的研究及应用.华北电力大学硕士论文,2006.
[117]张云生,祝晓红.自适应控制器设计及应用.北京:国防工业出版社,2005.
[118]韩志刚,蒋爱平,王洪桥.自适应辨识预报和控制:多层递阶途径.黑龙江:黑龙江教育出版社,1995.
[119]秦滨,韩志刚.一种非线系统自适应控制及其收敛性分析.控制理论与应用,1996,13(5):657-662.
[120]Hou Z S,H H W.The model-free learning adaptive control of a class siso nonlinear systems.Proceddings of the American Control Conference,Albuquerque,1997:343-344.
[121]Hou Z S,Han C,Huang W H.The model-free learning adaptive control of a class miso nonlinear discrete-time systems.IFAC Low Cost Automation,Shenyang,P.R.China,1998:227-232.
[122]李秀英,蒋爱平,韩志刚.无模型控制方法的抗干扰能力分析.Proceddings of the 25th Chinese Control Conference,2006:5-10.
[123]张铁柱,宋仁学,韩志刚.离散时间非线性系统线性化的泛模型方法.控制与决策,2002,17(2):249-251.
[124]韩志刚,徐明新.无模型控制律的一般形式及其在石化工业中的应用.黑龙江大学自然科学学报,2001,18(3):24-29.
[125]Ljung L.Convergence analysis of parametric identification methods.IEEE Trans.Autom.Control,AC-23,1978,(5):770-783.
[126]谢新民,丁锋.自适应控制系统.北京:清华大学出版社,2002.
[127]Ljung L.System identification:theory for the user.Englewood Cliffs,NJ:Prentice-Hall,1999.
[128]Ding F,Xie X M,Fang C Z.Multi-innovation identification methods for time-varying systems.Acta Automatica Sinica,1996,22(1):85-91.
[129]丁韬.多新息辨识方法的研究.清华大学硕士论文,2002.
[130]Ding F,Chen T W.Multi-innovation stocahstic gradient identification methods.Proceedings of the sixth World Congress on Intelligent Control and Automation,Dalian,China,2006:1501-1505.
[131]Ding F,Chen T W.Performance analysis of multi-innovation gradient type identification methods.Automatica,2007,43(1):1-14.
[132]丁锋,丁韬.时变多变量系统多新息投影算法的均方收敛性.湖北工学院学报,2001,16(4):16-20.
[133]Ding F,Ding T.Mean square convergence of multi-innovation forgetting gradient identification.IEEE Pacific Rim Conference on Communications,Computers and Signal Processing,2001:437-440.
[134]林鹏,杨波.随机逼近及自适应算法.北京:科学出版社,2003.
[135]高尚,杨静宇.群智能算法及其应用.北京:中国水利水电出版社,2006.
[136]Kennedy J,Eberhart R.Particle swarm optimization.Proceedings of IEEE International Conference on Neural Networks,New York,1995,4:1942-1948.
[137]谢晓峰.微粒群算法综述.控制与决策,2002,18(2):129-134.
[138]Shi Y,Eberhart R.A modified particle swarm optimizer.IEEE World Congress on Computational Intelligence,1998:69-73.
[139]Kennedy J.The particle swarm:social adaptation of knowledge.Proc.IEEE Int.Conf.on Evolutionary Computation,Indiamapolis,1997:303-308.
[140]李晓磊.一种新型的智能优化方法—人工鱼群算法.浙江大学博士学位论文,2003.
[141]李晓磊,钱积新.一种基于动物自治体的寻优模式:鱼群算法.系统工程理论与实践,2002,(11):32-38.
[142]Narendra K S,Parthasarathy K.Identification and control of dynamical systems using neural networks.IEEE Trans.on Neural Networks,1990,1:4-27.
[143]谭永红.基于bp神经网络的自适应控制.控制理论与应用,1994,11(1):80-84.
[144]Wigren T.Recursive prediction error identification using the nonlinear wiener model.Automatics.,1993,29(4):1011-1025.
[145]Wang L.Open architecture-based factory automation systems.IEEE Rospace and Electronic Systems Magazine,2004,19(2):14-17.
[146]OSACA.Open sytem architecture for controls within automation systems.ESPRIT Ⅲ Project 6379/9115,1996.
[147]易凡,徐华,王家廒等.开放式工业自动化控制系统的软件框架.计算机工程与应用,2007,43(13):228-232.
[148]秦品乐.J2EE构架电子商务系统中持久层的设计与实现.大连理工大学硕士论文,2005.
[149]孙承猛,刘寅东.船舶浮态计算的一种优化方法.大连海事大学学报,2006,32(2):39-41.
[150]盛振邦,杨尚荣,陈雪深.船舶静力学.上海:上海交通大学出版社,1991.
[151]柴天佑.多变量自适应解耦控制及应用.北京:科学出版社,2001.
[152]Wittenmark B,Middleton R,Goodwin G.Adaptive decoupling of multivariable systems.International Journal of Control,1987,46(6):1993-2009.
[2]川原靖夫.智能化船的液货装卸系统自动化.船舶,1991,6:37-39.
[3]Wiener N.Extrapolation,Interpolation,and Smoothing of Stationary Time Series,with Engineering Application.New York:Tech.Press of M.I.T.and John Wiley and Sons,1949.
[4]Wiener N.Cybernetics or Control and Communication in the Animal and the Machine.New York:Tech.Press of M.I.T.and John Wiley and Sons,1961.
[5]张有为.维纳与卡尔曼滤波理论导论.北京:人民教育出版社,1980.
[6]王宏禹,邱天爽.自适应噪声抵消与时间延迟估计.大连:大连理工大学出版社,1999.
[7]Carter G C,etal.Estimation of magnitude-sguare coherence function via overlapped fast fourier transform processing.IEEE Trans.Audio and Electroacoustics,1973,21(4):337-344.
[8]Bendat J S,Piersol A G.Engineering apphcation of correlation and spectral analysis,2nd Edition.Willey,USA,1993.
[9]李启虎.自适应噪声抵消滤波器抵消能力的研究.电子学报,1981,4:16-24.
[10]韩新洁,孟宪尧.船舶液位测量中海浪的滤波处理.大连海事大学学报,1999,25(1):11-14.
[11]Cooley J,Lewis P A.An algorithm for the machine calculation of comples fourier series.Mathematics of Computation,1965,19(90):95-139.
[12]Portnoff M.Time-frequency representation of digital signals and systems based on short-time fourier analysis.IEEE Transactions on Acoustics,Speech,and Signal Processing,1980,28(1):55-69.
[13]Cvetkovic Z.On discrete short-time fourier analysis.IEEE Transactions on Signal Processing,2000,48(9):2628-2640.
[14]谢杰成,张大力,徐立文.小波图象去噪综述.中国图象图形学报,2002,7(3):209-217.
[15]Mallat S.A theory for multiresolution signal decomposition:the wavelet representation.IEEE Transactions on PAMI,1989,11(7):674-693.
[16]Mallat S.Characterization of signals from nultiscale edges.IEEE Transactions on PAMI,1992,14(7):710-732.
[17]彭玉华,汪文秉.小波用于估测散射波波达时间及去噪.电子学报,1996,24(4):113-116.
[18]刘朝山,张维强等.一种改善小波变换模极大值重构信号的整体变分方法.电子学报,2004,32(10):1713-1715.
[19]Donoho D L,Johnstone M.Ideal spatial adaption by wavelet shrinkage.Biometrika,1994,81(3):425-455.
[20]Krim H,Schick C.Minmax description length fro signal denoising and optimized representation.IEEE Trans.Information Theory,1999,45(3):898-908.
[21]Xie J,Zhang D,Xu W.Spatially adaptive wavelet denoising using the minimum description length principle.IEEE Trans.on Image Processing,2004,13(2):179-187.
[22]Sun W,Palazoglu A.Detecting abnormal process trends by wavelet-domain hidden markov models.AICHE Journal,2003,49(1):140-150.
[23]于德介,程军圣.机械故障诊断的Hilbert-Huang变换方法.北京:科学出版社,2007.
[24]Huang N E,Steven Z S,Long R,etal.The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis.Proc.R.Soc.A,1998,454:903-995.
[25]Huang N E,Shen Z,Long S R.A new view of nonlinear water waves:the hilbert spectrum.A.Rev.Fluid Mech.,1999,31:417-457.
[26]Huang N E,Shih H H,Shen Z,etal.The ages of large-amplitude coastal seiches on the caribbean coast of puerto rico.Journal Phys.Oceanogr,2000,30:2001-2012.
[27]王卫强,陈宗镛,左军成.经验模态法在中国沿岸海平面变化中的应用研究.海洋学报,1999,21(6):102-109.
[28]Deng Y,Wang W.Boundary processing technique in emd method and hilbert transform.Chinese Science Bulletin,2001,46(11):257-263.
[29]Rillng G,Flandrin P,etal.On empirical mode decomposition and its algorithms.IEEE EURASIP Workshop on Nonlinear Signal and Image Processing(I),2003,June:9-11.
[30]蒋爱平,李秀英,韩志刚.从pid到无模型控制器.控制工程,2005,12(3):217-220.
[31]Rohrs C E,Valavani L,Athans M,etal.Analytical verification of undesirable properties of direct model reference adaptive control algorithms.Proc.20th IEEE CDC,1981:1272-1284.
[32]Richalet J,Rault A,Testud J L,etal.Model predictive heuristic control:application to industrial processes.Automatica,1976,14:413-428.
[33]Garcia.G E,Morshedi A M.Quadratic programming solution of dynamic matrix control.Chem.Eng.Commun.,1981,46(1):73-87.
[34]Clarke E W,Mohtadi C,Tuffs P S.Generalized preditive control.Automatica,1987,23(2):137-162.
[35]Manfred M,Jay H L.Model predictive control:past,present and future.Computers and Chemical Engineering,1999,23:667-682.
[36]Zadeh L A.Fuzzy sets.Information and Control,1965,8:338-353.
[37]罗熊,孙增圻.计算智能方法优化设计模糊控制系统:现状与展望.控制与决策,2007,22(9):961-966.
[38]Raju G V S.Adaptive hierarchical fuzzy controller.IEEE Trans.on Systems,Man and Cybemetics,1993,23(4):973-980.
[39]Linkens D A,Nie J Constructing rule-based for multivariable fuzzy control by self-learning.Int.J.System Science,1993,24(1):111-127.
[40]Wang L X.Fuzzy systems as universal approximators.IEEE Int.Conf.Fuzzy Systems,San Diego,USA,1992,2(2):162-176.
[41]Castro J L,Pelgado M.Fuzzy systems with defuzzification are universal approximators.IEEE Trans.on Systems,Man and Cybernetics,1996,26(1):149-152.
[42]Sun Z.Controller design and stability analysis of time-continuous systems based on a fuzzy state model.Acta Automatics.Sinica,1998,24(2):212-216.
[43]张恩勤,施颂椒等.模糊控制系统近年来的研究与发展.控制理论与应用,2001,18(1):7-11.
[44]Hunt K J,D S,etal.Neural network for control system:a survey.Automatica,1992,28(6):1083-1112.
[45]Narendra K S.Identifcation and control of dynamic systems using neural networks.IEEE Transactions on Neural Networks,1990,(1):4-27.
[46]黄道平.多变量非线性过程控制—预测、神经网络、解耦控制方法.华南理工大学博士学位论文,1998.
[47]Sorensen P H,Morgaard M,Ravn O,etal.Implementation of neural network based non-linear predictive control.Neurocomputing,1999,28:37-51.
[48]Hunt K J,Sbarbaro D.Neural networks for nonlinear interanl model control.Proc.IEE PID,1991,138(2):431-438.
[49]Chen F C,Khalil H K.Adaptive control of a class of nonlinear discrete-time systems using neural networks.IEEE Trans.on Automatic Control,1995,40(5):791-801.
[50]Jang J S R.Self-learning fuzzy controllers based on temporal back propagation.IEEE Trans.on Neural Network,1992,3(5):723-774.
[51]Barto A G,Sutton R S,Anderson C W.Neuronlike adaptive elements that can solve difficult learning control problems.IEEE Trans.on Systems,Man and Cybernetics,1987,13(5):834-846.
[52]邵世煌,龚道勇,赵杏弟.一个基于“类神经元”模型的智能控制系统及其在柔性臂上的研究.控制与决策,1990,5(2):32-37.
[53]舒怀林.Pid神经元网络控制系统分析.自动化学报,1999,25(1):105-111.
[54]王宁.神经元网络控制理论及应用研究.华中理工大学博士学位论文,1992.
[55]王宁,涂建.电渣重熔过程的神经元智能控制.自动化学报,1993,19(5):634-636.
[56]Cheng G S.Model-free adaptive(mfa) control.IEE Computing and Control Engineering,2004,15(3):28-33.
[57]侯忠生,韩志刚.非线性系统鲁棒无模型学习自适应控制.控制与决策,1995,10(2):137-142.
[58]Arimoto S,Kawamura S,Miyazaki F.Bettering operation of robotics by learning.Journal of Robotic System,1984,1(2):123-140.
[59]Safonov M G,Tsao T C.The unfalsified control concept and leering.IEEE Trans.Atuom.Control,1997,42(6):843-847.
[60]韩志刚.一类无模型系统的控制律及其仿真研究.第一届全球华人智能控制与智能自动化大会论文集,北京:科学出版社,1993:2603-2608.
[61]侯忠生.非线性系统参数辨识、自适应控制和无模型学习自适应控制.东北大学博士论文,1994.
[62]韩志刚,王德进.无模型控制器.黑龙江大学自然科学学报,1994,11(4):29-35.
[63]张铁柱.无模型控制理论及其应用.东北大学博士论文,2002.
[64]韩志刚.无模型控制器的基本形式及其应用效果分析.第三届全球华人智能控制与智能自动化大会论文集,合肥:中国科学技术大学出版社,2000:2902-2906.
[65]韩志刚,蒋爱平,汪国强.无模型控制方法对多变量耦合系统控制的研究.控制与决策,2004,19(10):1155-1158.
[66]王玲,张明波.多步时滞非线性多输入系统的无模型控制律的一种改进算法.黑龙江大学自然科学学报,1997,14(3):31-35.
[67]曹荣敏,侯忠生.无模型控制方法在直线电机控制中的仿真研究.系统仿真学报,2006,18(10):2874-2877.
[68]张铁柱,汪国强.无模型控制器在焦化炉上的应用效果分析.第21届中国控制会议文集,杭州,2002:265-268.
[69]马洁.船舶运动姿态预报与控制方法研究.哈尔滨工程大学博士论文,2006.
[70]韩志刚.无模型控制方法在化肥生产中的应用.控制理论与应用,2004,21(6):858-863.
[71]韩志刚,蒋爱平,汪国强.合成氨生产中的氢氮比控制系统.控制理论与应用,2005,22(5):762-766.
[72]孟宪尧.数据融合技术与船舶自动化.大连:大连海事大学出版社,2003.
[73]Richard B,Christopher W F.Cargo pump performance versus design criteria.Naval Engineers Journal,1998,4:47-60.
[74]Tviberg G.The new generation product tanker and its cargo pumping system.Brodogradnja,2001,49:329-332.
[75]肖民,姚寿广等.船舶压载水系统仿真建模型与控制的研究与实现.系统仿真学报,2005,17(12):3036-3039.
[76]吴宛青.油船货油阀液压系统的建模与数据仿真.大连海事大学学报,1999,25(1):349-38.
[77]齐国清.油船舱内液面高度测量雷达系统.中国造船,1997,137(2):96-100.
[78]徐邦林.液化天然气船舶液货系统的仿真研究.大连海事大学硕士论文,2002.
[79]杨立,林焰,陈明等.成品油船货油装卸系统仿真研究.中国造船,2006,47(2):82-88.
[80]于鹏.LonWorks总线在船舶油舱监控系统中的应用研究.大连海事大学硕士论文,2005.
[81]杨光.现代油船液货自动装卸的仿真控制研究.大连海事大学硕士论文,2005.
[82]Sato K,Itoh H,Awashima.Y.Expert system for oil tanker loading/unloading operation planning.Applications in the Automation of Shipyard Operation and Ship Design,IFIP,1992:82-88.
[83]Villabo M.Liquid,cargo handling training simulator.Norwegian Maritime Research,1984,12:30-35.
[84]Jakobsen B K,Miller E R,Alman P.The u.s.merchant marine academy liquid cargo handling simulator.Marine Technology,1990,27:257-264.
[85]吴宛青,殷佩海.油船货油装卸操作模拟器的系统设计.中国航海,1999,44:71-74.
[86]杨建立,吴宛青.油船货油系统的仿真研究.大连海事大学学报,1999,25(3):63-68.
[87]杨立.液货船货油装卸系统仿真及软件开发.大连理工大学硕士论文,2005.
[88]曾向明.大型油轮货油装卸模拟装置开发研究.上海海事大学硕士论文,2003.
[89]朱仁庆,王志东,方智勇.液舱内大幅晃荡引起的压强预报(英文).船舶力学,2004,8(6):71-78.
[90]程军圣.基于Hilbert-Huang变换的旋转机械故障诊断方法研究.湖南大学博士学位论文,2005.
[91]Mandel L.Interpretation of instantaneous frequency.Amer.J.Phys.,1974,4(2):840-846.
[92]Cohen.Time frequency analysis.Englewood Cliffs,NJ:Prentice Hall,1995.
[93]Boashash B.Estimating and interpreting the instantaneous frequency of a signal.Proc.of IEEE,1992,80(4):520-538.
[94]Long S R,Huang N E,Tung C E,etal.The hilbert techniques:an alternate approach for non-steady time series analysis.IEEE Geoscience Remote Sensing Soc,Let,1995,3:6-11.
[95]熊卫华.经验模态分解方法及其在变压器状态监测中的应用研究.浙江大学博士论文,2006.
[96]Chen Q H,Huang E,etal.A b-spline approach for empirical mode decompositions.Advances in Computational mathematics,2006,(24):171-195.
[97]杨世锡,胡劲松,吴昭同.基于高次样条插值的emd方法研究.浙江大学学报(工学版),2004,38(3):267-270.
[98]Huang D J,Zhao J P,Su J L.Practical implementation of hilbert-huang transform algorithm.Acta Oceanologica Sinica,2003,22(1):1-14.
[99]Rilling G,Flandrin P,P G.On empirical mode decomposition and its algorithms.IEEE EURASIP Workshop on Nonlinear Signal and Image Processing,Grado(Ⅰ),June 9-11,2003.
[100]张郁山,梁建文,胡聿贤.利用自回归模型处理emd方法中的边界问题.自然科学进展,2003,13(10):1054-1059.
[101]刘慧婷,张旻,程家兴.基于多项式拟合算法的emd端点问题的处理.计算机工程与应用,2004,(16).
[102]许宝杰,张建民,徐小力等.抑制emd端点效应方法的研究.北京理工大学学报,2006,26(3):196-200.
[103]谭善文.多分辨Hilbert-Huang变换方法的研究.重庆大学博士论文,2001.
[104]郑天翔,杨力华.经验模式分解算法的探讨和改进.中山大学学报,2007,46(1):1-6.
[105]余泊.自适应时频分析方法及其在故障论断中的应用.大连理工大学博士论文,1998.
[106]盖强.局域波时频分析方法的理论研究与应用.大连理工大学博士论文,2001.
[107]施法中.计算机辅助几何设计与非均匀有理B样条.北京:高等教育出版社,2005.
[108]Liu B,Riemenschneider S,Y X.Gearbox fault diagnosis using empirical mode decomposition and hilbert spectrum.Mechanical Systems and Signal Processing,2006,20:718-734.
[109]申丽然.Hilbert-Huang变换及其在含噪语音信号处理中的应用研究.哈尔滨工程大学博士论文,2006.
[110]赵进平.异常事件对emd方法的影响及其解决方法研究.青岛海洋大学学报,2001,31(6):805-814.
[111]Li H L,Yang L H.The study of the intermittency test filtering character of hilbert huang transform.Matheratics stud computers in simulation,2005,70:22-32.
[112]Donoho D L.De-noising by soft-thresholding.IEEE Transactions on Information Theory,1995,41(3):613-627.
[113]Coifman R R,Donoho D L.Translation-invariant de-noising.Wavelets and Statistics,1995,103:125-150.
[114]汤宝平,杨昌棋,谭善文等.基于平移不变的小波去噪方法及应用.重庆大学学报,2002,25(3):1-5.
[115]侯忠生.无模型自适应控制的现状与展望.控制理论与应用,2006,23(4):586-591.
[116]李伟.无模型控制器的研究及应用.华北电力大学硕士论文,2006.
[117]张云生,祝晓红.自适应控制器设计及应用.北京:国防工业出版社,2005.
[118]韩志刚,蒋爱平,王洪桥.自适应辨识预报和控制:多层递阶途径.黑龙江:黑龙江教育出版社,1995.
[119]秦滨,韩志刚.一种非线系统自适应控制及其收敛性分析.控制理论与应用,1996,13(5):657-662.
[120]Hou Z S,H H W.The model-free learning adaptive control of a class siso nonlinear systems.Proceddings of the American Control Conference,Albuquerque,1997:343-344.
[121]Hou Z S,Han C,Huang W H.The model-free learning adaptive control of a class miso nonlinear discrete-time systems.IFAC Low Cost Automation,Shenyang,P.R.China,1998:227-232.
[122]李秀英,蒋爱平,韩志刚.无模型控制方法的抗干扰能力分析.Proceddings of the 25th Chinese Control Conference,2006:5-10.
[123]张铁柱,宋仁学,韩志刚.离散时间非线性系统线性化的泛模型方法.控制与决策,2002,17(2):249-251.
[124]韩志刚,徐明新.无模型控制律的一般形式及其在石化工业中的应用.黑龙江大学自然科学学报,2001,18(3):24-29.
[125]Ljung L.Convergence analysis of parametric identification methods.IEEE Trans.Autom.Control,AC-23,1978,(5):770-783.
[126]谢新民,丁锋.自适应控制系统.北京:清华大学出版社,2002.
[127]Ljung L.System identification:theory for the user.Englewood Cliffs,NJ:Prentice-Hall,1999.
[128]Ding F,Xie X M,Fang C Z.Multi-innovation identification methods for time-varying systems.Acta Automatica Sinica,1996,22(1):85-91.
[129]丁韬.多新息辨识方法的研究.清华大学硕士论文,2002.
[130]Ding F,Chen T W.Multi-innovation stocahstic gradient identification methods.Proceedings of the sixth World Congress on Intelligent Control and Automation,Dalian,China,2006:1501-1505.
[131]Ding F,Chen T W.Performance analysis of multi-innovation gradient type identification methods.Automatica,2007,43(1):1-14.
[132]丁锋,丁韬.时变多变量系统多新息投影算法的均方收敛性.湖北工学院学报,2001,16(4):16-20.
[133]Ding F,Ding T.Mean square convergence of multi-innovation forgetting gradient identification.IEEE Pacific Rim Conference on Communications,Computers and Signal Processing,2001:437-440.
[134]林鹏,杨波.随机逼近及自适应算法.北京:科学出版社,2003.
[135]高尚,杨静宇.群智能算法及其应用.北京:中国水利水电出版社,2006.
[136]Kennedy J,Eberhart R.Particle swarm optimization.Proceedings of IEEE International Conference on Neural Networks,New York,1995,4:1942-1948.
[137]谢晓峰.微粒群算法综述.控制与决策,2002,18(2):129-134.
[138]Shi Y,Eberhart R.A modified particle swarm optimizer.IEEE World Congress on Computational Intelligence,1998:69-73.
[139]Kennedy J.The particle swarm:social adaptation of knowledge.Proc.IEEE Int.Conf.on Evolutionary Computation,Indiamapolis,1997:303-308.
[140]李晓磊.一种新型的智能优化方法—人工鱼群算法.浙江大学博士学位论文,2003.
[141]李晓磊,钱积新.一种基于动物自治体的寻优模式:鱼群算法.系统工程理论与实践,2002,(11):32-38.
[142]Narendra K S,Parthasarathy K.Identification and control of dynamical systems using neural networks.IEEE Trans.on Neural Networks,1990,1:4-27.
[143]谭永红.基于bp神经网络的自适应控制.控制理论与应用,1994,11(1):80-84.
[144]Wigren T.Recursive prediction error identification using the nonlinear wiener model.Automatics.,1993,29(4):1011-1025.
[145]Wang L.Open architecture-based factory automation systems.IEEE Rospace and Electronic Systems Magazine,2004,19(2):14-17.
[146]OSACA.Open sytem architecture for controls within automation systems.ESPRIT Ⅲ Project 6379/9115,1996.
[147]易凡,徐华,王家廒等.开放式工业自动化控制系统的软件框架.计算机工程与应用,2007,43(13):228-232.
[148]秦品乐.J2EE构架电子商务系统中持久层的设计与实现.大连理工大学硕士论文,2005.
[149]孙承猛,刘寅东.船舶浮态计算的一种优化方法.大连海事大学学报,2006,32(2):39-41.
[150]盛振邦,杨尚荣,陈雪深.船舶静力学.上海:上海交通大学出版社,1991.
[151]柴天佑.多变量自适应解耦控制及应用.北京:科学出版社,2001.
[152]Wittenmark B,Middleton R,Goodwin G.Adaptive decoupling of multivariable systems.International Journal of Control,1987,46(6):1993-2009.