轧机HAGC系统辨识与鲁棒控制研究
|
摘要
液压伺服系统是目前工业、军事、航空航天等领域装备中的关键部件，其静、动态特性直接影响到设备的性能。随着对其动静态响应、输出功率密度和精度的要求不断提高，其结构动力学性能以及负载扰动、参数变化等不确定性因素的影响已成为不可忽略的问题，这些问题对液压伺服系统的结构设计和控制设计提出了巨大的挑战。本论文依托国家重大科研项目，面向重大工程的实际需求，从液压伺服系统的机理建模和鲁棒参数辨识、控制方法设计到原理样机实现三个方面展开研究，旨在通过鲁棒迭代控制来提高液压伺服系统的性能，使其满足制造和装配领域设备日益苛刻的性能需求。
本文针对具有不确定性的轧机HAGC液压伺服系统，围绕其动力学建模与鲁棒控制中的关键问题，采用理论推导-仿真分析-实验测试相结合的方法，建立了轧机HAGC系统的参数化数学模型，研究了基于已知模型的鲁棒迭代学习控制器，并通过搭建相关测试实验平台进行了验证。在分析了轧机HAGC系统结构组成和体系特点的基础上，推导了轧机HAGC的机理模型，针对系统的不确定性，提出了一种基于Hardy空间的二段式非线性鲁棒辨识方法，得到一个参数待估的可行参数集合，建立其含参数不确定性的系统模型集，确保真实系统落在该模型集中。为了实现高精度高效率的运动，提出了一种基于斜坡正弦波函数的平滑轨迹规划方法，可以综合考虑系统的快速性和平稳性。
针对液压伺服系统重复性工作空间，引入了反馈－前馈形式的迭代学习算法，设计了基于脉冲响应矩阵的迭代学习控制策略，使迭代学习控制器能够更好地反映系统动态特性，并且根据系统的误差输出来求取所需的控制量，刷新到迭代学习前馈指令中。综合考虑液压伺服系统模型的不确定性和系统稳定性指标、抗干扰指标，研究了基于定量反馈理论的液压伺服系统鲁棒控制器设计问题。讨论了适合液压伺服系统的迭代学习控制器(ILC）的设计方法，提出了一种基于H_∞法的鲁棒ILC设计方法，分析了该方法的可解性，推导出了误差收敛的充要条件。鲁棒ILC方法不仅将迭代学习控制器的综合问题转化为H_∞最(次）优控制器的综合问题，还可通过选择适当权函数，明确处理过程中不确定性因素。鲁棒ILC方法可通过μ综合方法来进行求解，使学习性能最大化。根据所提出的设计方法设计出了标称ILC和鲁棒ILC，在实验中分别执行了这两种ILC，根据实验结果对两种ILC进行了对比分析，实验结果验证了所提出设计方法的有效性。
本文的研究成果成功地应用于轧机液压自动厚度控制(Hydraulic Automatic GaugeControl，HAGC)，完成了HAGC测试平台的搭建和控制系统软硬件设计，并在测试平台上成功进行了辨识和控制实验，为HAGC系统的结构设计和控制提供了分析依据和设计指导。
Hydraulic servo systems are the important subsystems for the industrial, military andaerospace manufacturing. The static and dynamic characteristics of hydraulic servo systemsaffect the equipment performance directly. The structural dynamics, load disturbance andparameter changes of hydraulic servo systems cannot be ignored, with the increasingrequirements of quick response, output power density and motion precision. All of these posedtremendous challenges on the structure design and motion control of hydraulic servo system. Inorder to fulfill the urgent requirements of state key scientific research and engineering projects,the modeling and robust identification of hydraulic servo system, design of robust iterativelearning control and realization of principle prototype are studied in this dissertation. Throughthe characteristics analysis and controller synthesis, the performance of hydraulic servo system isimproved to meet the increasingly stringent performance requirements.
In this thesis, the key issues of dynamic modeling and robust control for the hydraulic servosystem are studied. The parameterized mathematical model of hydraulic servo system isestablished by the combination method of theoretical analysis, simulation and experimentaltesting. The design of the controllers takes into account several aspects of the system’s dynamicsare completed. The proposed optimal approach is validated by the component level experimentalmeasurement. On the basis of analysis of the hydraulic servo system structures and systemcharacteristics，the mechanism model of the hydraulic servo system is derived. The two-stepnonlinear robust identification method based on Hardy space is proposed for the uncertainties ofthe system. The feasible parameter set of model parameters is obtained. The system model withparameter uncertainty set is established, to ensure that the real system falls on the model set. Inorder to achieve high precision and high efficiency motion, a smooth trajectory planning methodbased on ramp sinusoid functions, which can take the rapidity and smoothness of movement intoaccount.
A feedback-feedforward form of iterative learning algorithms is introduced for the repetitivework space of hydraulic servo system. Iterative learning control strategy based on the impulseresponse matrix is designed to reflect the dynamic characteristics. According to the error of thesystem output to achieve the control signal, and refresh to iterative learning feed-forward order.Based on the quantitative feedback theory, the robust position controller for a hydraulic servosystem is studied, which considering model uncertain, stability and suppress disturbance ofmodel.
Iterative learning control (ILC) design method for hydraulic servo system is discussed. Arobust ILC design method based H_∞method on is presented, and solvability of the method isanalyzed, the necessary and sufficient conditions of error convergence are derived. The robustILC method not only transformed iterative learning controller synthesis into most (sub) optimalcontroller design, but also deal with the uncertainties by selecting the appropriate weightfunction. The robust ILC method can be solved by the μ synthesis methods to maximize thelearning performance. The nominal ILC and the robustness ILC are design and performed in the experiment. The two ILC experimental results are compared, and the effectiveness of theproposed method is verified.
The research results are applied to a hydraulic automatic gauge control (HAGC) systemsuccessfully. The hardware and software system of HAGC test platform are designed andimplemented. The identification experiments and control experiments are completed in the testplatform. The proposed methods can be used to guide the controller design for HAGC.
本文针对具有不确定性的轧机HAGC液压伺服系统，围绕其动力学建模与鲁棒控制中的关键问题，采用理论推导-仿真分析-实验测试相结合的方法，建立了轧机HAGC系统的参数化数学模型，研究了基于已知模型的鲁棒迭代学习控制器，并通过搭建相关测试实验平台进行了验证。在分析了轧机HAGC系统结构组成和体系特点的基础上，推导了轧机HAGC的机理模型，针对系统的不确定性，提出了一种基于Hardy空间的二段式非线性鲁棒辨识方法，得到一个参数待估的可行参数集合，建立其含参数不确定性的系统模型集，确保真实系统落在该模型集中。为了实现高精度高效率的运动，提出了一种基于斜坡正弦波函数的平滑轨迹规划方法，可以综合考虑系统的快速性和平稳性。
针对液压伺服系统重复性工作空间，引入了反馈－前馈形式的迭代学习算法，设计了基于脉冲响应矩阵的迭代学习控制策略，使迭代学习控制器能够更好地反映系统动态特性，并且根据系统的误差输出来求取所需的控制量，刷新到迭代学习前馈指令中。综合考虑液压伺服系统模型的不确定性和系统稳定性指标、抗干扰指标，研究了基于定量反馈理论的液压伺服系统鲁棒控制器设计问题。讨论了适合液压伺服系统的迭代学习控制器(ILC）的设计方法，提出了一种基于H_∞法的鲁棒ILC设计方法，分析了该方法的可解性，推导出了误差收敛的充要条件。鲁棒ILC方法不仅将迭代学习控制器的综合问题转化为H_∞最(次）优控制器的综合问题，还可通过选择适当权函数，明确处理过程中不确定性因素。鲁棒ILC方法可通过μ综合方法来进行求解，使学习性能最大化。根据所提出的设计方法设计出了标称ILC和鲁棒ILC，在实验中分别执行了这两种ILC，根据实验结果对两种ILC进行了对比分析，实验结果验证了所提出设计方法的有效性。
本文的研究成果成功地应用于轧机液压自动厚度控制(Hydraulic Automatic GaugeControl，HAGC)，完成了HAGC测试平台的搭建和控制系统软硬件设计，并在测试平台上成功进行了辨识和控制实验，为HAGC系统的结构设计和控制提供了分析依据和设计指导。
Hydraulic servo systems are the important subsystems for the industrial, military andaerospace manufacturing. The static and dynamic characteristics of hydraulic servo systemsaffect the equipment performance directly. The structural dynamics, load disturbance andparameter changes of hydraulic servo systems cannot be ignored, with the increasingrequirements of quick response, output power density and motion precision. All of these posedtremendous challenges on the structure design and motion control of hydraulic servo system. Inorder to fulfill the urgent requirements of state key scientific research and engineering projects,the modeling and robust identification of hydraulic servo system, design of robust iterativelearning control and realization of principle prototype are studied in this dissertation. Throughthe characteristics analysis and controller synthesis, the performance of hydraulic servo system isimproved to meet the increasingly stringent performance requirements.
In this thesis, the key issues of dynamic modeling and robust control for the hydraulic servosystem are studied. The parameterized mathematical model of hydraulic servo system isestablished by the combination method of theoretical analysis, simulation and experimentaltesting. The design of the controllers takes into account several aspects of the system’s dynamicsare completed. The proposed optimal approach is validated by the component level experimentalmeasurement. On the basis of analysis of the hydraulic servo system structures and systemcharacteristics，the mechanism model of the hydraulic servo system is derived. The two-stepnonlinear robust identification method based on Hardy space is proposed for the uncertainties ofthe system. The feasible parameter set of model parameters is obtained. The system model withparameter uncertainty set is established, to ensure that the real system falls on the model set. Inorder to achieve high precision and high efficiency motion, a smooth trajectory planning methodbased on ramp sinusoid functions, which can take the rapidity and smoothness of movement intoaccount.
A feedback-feedforward form of iterative learning algorithms is introduced for the repetitivework space of hydraulic servo system. Iterative learning control strategy based on the impulseresponse matrix is designed to reflect the dynamic characteristics. According to the error of thesystem output to achieve the control signal, and refresh to iterative learning feed-forward order.Based on the quantitative feedback theory, the robust position controller for a hydraulic servosystem is studied, which considering model uncertain, stability and suppress disturbance ofmodel.
Iterative learning control (ILC) design method for hydraulic servo system is discussed. Arobust ILC design method based H_∞method on is presented, and solvability of the method isanalyzed, the necessary and sufficient conditions of error convergence are derived. The robustILC method not only transformed iterative learning controller synthesis into most (sub) optimalcontroller design, but also deal with the uncertainties by selecting the appropriate weightfunction. The robust ILC method can be solved by the μ synthesis methods to maximize thelearning performance. The nominal ILC and the robustness ILC are design and performed in the experiment. The two ILC experimental results are compared, and the effectiveness of theproposed method is verified.
The research results are applied to a hydraulic automatic gauge control (HAGC) systemsuccessfully. The hardware and software system of HAGC test platform are designed andimplemented. The identification experiments and control experiments are completed in the testplatform. The proposed methods can be used to guide the controller design for HAGC.
引文
[1] Gevers M. A Personal View of Development of System Identification[J]. IEEE ControlSystems Magazine,2006,26(6):93~105.
[2] Gevers M. Connecting Identification and Robust Control[J]. The Modeling of Uncertaintyin Control Systems,1994,192:35~37.
[3]冯旭，孙优贤等.鲁棒辨识问题评述[J].控制理论与应用,1993,10(6):609~616.
[4] Ljung L. Stable Identification: Theory for the Users[M]. New Jersey: Prentice-Hall Inc.,1987.
[5] Fogel E. System Identification via Membership Set Constraints with Energy ConstrainedNoise [J]. IEEE Transactions on Automatic Control,1979,24(5):752~758.
[6]王文正，蔡金狮.飞行器气动参数的集员辨识[J].宇航学报,1998,19(2):31~36.
[7]徐长江，宋文忠.基于小波变换的分频集员辨识[J].控制与决策,1999,14(1):48~52.
[8]柴伟，孙先仿.一种非线性系统集员辨识算法[J].北京航空航天大学学报,2005,31(11):1237~1244.
[9]王晓军，邱志平.线性时不变系统集员辨识的区间算法[J].力学学报,2005,37(6):713~718.
[10] Alamo T, Bravo J M, Redondo M J. A Set Membership State Estimation Algorithm Basedon DC Function[J]. Automatica,2008,44(1):216~224.
[11]和丽清，孙先仿，邱红专.非线性系统的结构选择及其参数的集员辨识[J].北京航空航天大学学报,2010,36(10):1189~1193.
[12] Reppa V, Vagia M, Tzes A. Fault Detection Using Set Membership Identification forMicro-Electrostatic Actuators[C].16th IEEE International Conference on ControlApplications, Piscataway,2007.
[13] Ingimundarson A, Bravo J M, Puig V. Robust Fault Diagnosis Using Parallelotope-BasedSet-Membership Consistency Tests[C]. Proceedings of the44th IEEE Conference onDecision and Control, Piscataway,2005.
[14] Chen J. Frequency Domain Tests for Validation of Linear Fractional Uncertain Model[J].IEEE Transactions on Automatic Control,1997,42(6):748~760.
[15] Parrilm P A, Sznaier M. Mixed Time/Frequency Domain Based Robust Identification[J].Automatica,1998,34(11):1375~1389.
[16] Helmicki J A, Jacobson C A, Nett C N. A Worst Case Deterministic Approach in H1[J].IEEE Transactions on Automatic Control,1999,6(10):1163~1176.
[17] Gu G X, Khargonekar P P. A Class of Algorithms for Identification in H∞[J]. Automatica,1992,28:299~312.
[18] Bai E W, Raman S. Robust System Identification with Noisy Experimental DataProjection Operator and Linear Algorithms[J]. Automatica,1994,30(7):1203~1206.
[19] Zoltan S, Jozsef B, Ferenc S. Identification of Rational Approximate Models in H∞UsingGeneralized Orthonormal Basis[J]. IEEE Transactions on Automatic Control,1999,44(1):153~158.
[20] Xiong Y, Peng J X, Ding M Y. Algorithm for Identification of Objects in a ComplexBackground[C]. Proceedings of SPIE International Society for Optical Engineering, SanDiego,1995.
[21] Akcay H. General Orthonormal Basis for Robust Identification in H∞[C]. Proceedings ofthe41st IEEE Conference on Decision&Control, Nevada,2002.
[22] Mario T, Michele T. H∞Set Membership Identification: A Survey[C]. Proceedings of the43rd IEEE Conference on Decision&Control, Nassau,2004.
[23] Chen J, Nett N. Worst-Case System Identification in H∞: Validation of a Priori inFormation, Essentially Optimal Algorithms and Error Bounds[C]. Proceedings of theAmerican Control Conference, Chicago,1994.
[24] Gu G X, Xiong D P, Zhou K M. Identifi in H∞Using Pick′s Interpolation[J]. System&Control Letter,1993,20(4):263~272.
[25] Jacobson C A, Nett C N, Partington J R. Worst-Case System Identification in l1: OptimalAlgorithms and Error Bounds[J]. System&Control Letter,1992,19(6):419~424.
[26] Li S P. A Recursive Interpolatory Algorithm for Robust Identification[C]. Proceedings ofthe5th World Congress on Intelligent Control and Automation, Hangzhou,2004.
[27] Chen J, Gu G. Worst Case Asymptotic Properties of Identification[J]. IEEE Transactionson Circuits and Systems,2002,49(4):437~446.
[28]李昇平，方京华，黄心汉.最坏情况下l1鲁棒辨识研究[J].控制与决策,1995,10(3):40~46.
[29]李昇平，方京华，黄心汉. l1鲁棒辨识收敛性态的研究[J].控制与决策,1996,11(1):71~77.
[30]李昇平. l1鲁棒辨识:一种递推插值方法[J].控制理论与应用,2002,19(6):67~73.
[31] Pena R S, Galarza C G. Practical Issues in Robust Identification[J]. IEEE Transactions onControl System Technology,1994,2(1):54~56.
[32] Chen J, Nett C N. The Caratheodory-Fejer Problem and H∞/l1Identification: A TimeDomain Approach[C]. Proceedings of the32nd IEEE Conference on Decision&Control,San Antonio,1993.
[33] Tong Z, Kimura H. Structure of Model Uncertainty for a Weakly Corrupted Plant[J]. IEEETransactions on Automatic Control,1995,40(4):639~655.
[34] Sanchez P, Sznaier M. Robust Identification with Mixed Time/Frequency Experiments:Consistency and Interpolation Algorithms[C]. Proceedings of the IEEE Conference onDecision&Control, Taejon,1995.
[35] Parrilo P A, Sznaier M, Sanchez P. Robust Mixed Time/Frequency Domain Based RobustIdentification[C]. Proceedings of the IEEE Conference on Decision&Control, Kobe,1996.
[36] Gu G X, Chen J. A Nearly Interpolatory Algorithm for H∞Identification with Mixed Timeand Frequency Response Data[J]. IEEE Transactions on Automatic Control,2001,46:464~469.
[37]窦立谦，宗群，刘文静.面向控制的系统辨识研究进展[J].系统工程与电子技术,2009,31(1):158~164.
[38] Davison E J. The Output Control of Linear Time Invariant Multivariable System withUnmeasurable Arbitrary Disturbances[J]. IEEE Transactions on Automatic Control,1972,17(5):621~630.
[39] Doyle J C, Stein G. Multivariable Feedback Design: Concepts for a Classical/ModernSynthesis[J]. IEEE Transactions on Automatic Control,1981,26(1):4~16.
[40] Packard A, Doyle J C. The Complex Structured Singular Value[J]. Automatica,1993,29(1):71~109.
[41]梅生伟，申铁龙，刘康志.现代鲁棒控制理论与应用[M].北京:清华大学出版社,2003.
[42]李新国，毛承元，陈红英. H∞制导律统计性能分析[J].西北工业大学学报,2004,1:27~30.
[43]查旭，崔平远，刘永才.导弹纵向机动鲁棒控制[J].航天控制,2003,4:44~48.
[44]李友年，贾晓红，王海波. H∞控制理论在空空导弹自动驾驶仪设计中的应用[J].航空兵器,2004,6:13~15.
[45] Zhang Y, Wu J G, Xu Y. The Application of H∞/Mixed Sensitivity Approach to AttitudeControl System for a Winged Missile[C]. Proceedings of the5th World Congress onIntelligent Control and Automation, Hangzhou,2004.
[46]张羽飞，冯汝鹏.巡航导弹成像器稳定装置H2/H∞混合最优PID控制器设计[J].航天控制,2003,3:8~13.
[47]姜世洲，洪华杰，纪明.基于QFT的光电稳定控制系统设计与分析[J].应用光学,2009,30(3):377~381.
[48] Haessing D, Decotiis J, Modern Control Methods Applied to a Line-of-Sight Stabilizationand Tracking System[C]. Proceedings of the American Control Conference, New York,1987.
[49]唐京珊.定量反馈理论及其在卫星姿态控制系统中的应用[D].哈尔滨,哈尔滨工业大学,2003.
[50]张庆振. QFT/TECS在飞机自动着陆控制中的应用研究[D].西安,西北工业大学,2004.
[51]晋严尊，杨一栋.空空导弹自适应飞控系统的定量反馈理论设计[J].南京航空航天大学学报,2001,33(1):86~90.
[52]刘小明.基于定量反馈理论的天线稳定平台跟踪控制系统设计[J].电光与控制,2009,16(10):83~86.
[53]宋勇，邓辉，张宗麟.定量反馈理论在侧向飞行控制系统中的应用[J].火力与指挥控制,2007,32(3):72~75.
[54]张福星，朱荣，熊威.基于定量反馈理论的微型飞行器增稳控制器[J].清华大学学报(自然科学版),2010,50(2):219~223.
[55] Arimoto S, Miyazaki F. Bettering operation of robotics by learning [J], J.Robotic System,1984,1(2):123~140.
[56] Hwang D H, Bien Z, Oh S R. Iterative learning control method for discreteeime dynamicsystems [C]. IEE Proceedings,1991,138(2):139~144.
[57] Tomohide N, Arimoto S. Learning control for robot tasks under geometric endpointconstraints [J]. IEEE Trans. On Robotics and Automation,1995,11(3):432~44.
[58]谢胜利，谢振东，韦岗等.非线性分布参数系统跟踪控制的学习算法[J].自动化学报，1999，25(5)：635~640.
[59] Yong F, Chow T W.2-D analysis for iterative learning controller for discrete-time systemswith variable initial conditions[J]. IEEE Trans.on Circuits and System I: FundamentalTheory and Applications,2003,50(5):722-727.
[60]孙涛.中厚板高精度厚度控制的研究与应用[D].沈阳,东北大学,2009.
[61]王小英.带钢热连轧精轧机组自动厚度控制系统研究[D].上海,上海交通大学,2008.
[62]郭迪. HC轧机的自动厚度控制[D].武汉,华中科技大学,2006.
[63]王国栋，刘相华，王军生.冷连轧生产工艺的进展[J].轧钢,2003,20(1):37~41.
[64] Bristow D A, Marina T, Andrew G A. A survey of iterative learning control: A learning-based method forhigh-performance tracking control [J]. IEEE Control Systems Magazine,2006,6(3):96–114.
[65] Chin I, Qin S J, Lee K S. A two-stage iterative learning control technique combined withreal-time feedback for independent disturbance rejection [J]. Automatica,2004,40(11):1913–1920.
[66] Chen C K, James H W. PD-type iterative learning control for trajectory tracking of apneumatic X-Y table with disturbances[C]. Proceedings of the2004IEEE InternationalConference on Robotics and Automation,2004,30(6):3500–3505.
[67] Levoci P A, Longman R W. Frequency domain prediction of final error due to noise inlearning and repetitive control [J]. Advances in the Astronautical Sciences,2002,112(2):1341–1359.
[68] Chen Y Q, Moore K L. A practical iterative learning path-following control of an omni-directional vehicle [J]. Asian Journal of Control,2002,4(1):90–98.
[69]陈奎生．液压与气压传动[M]．北京：机械工业出版社，1995
[70]湛从昌，傅连东，陈新元．液压可靠性与故障诊断[M]．北京：冶金工业出版社，2009．
[71]傅连东，朱学彪，李金良等．伺服缸测试系统的设计[J]．液压与气动，2006.01：30-32．
[72]王春行．液压控制系统[M]．北京：机械工业出版社，1995
[73]张莉松，胡祐德，徐立新．伺服系统原理与设计[M]．北京：北京理工大学出版社，2006．
[74]付曙光，陈奎生，湛从昌等．伺服液压缸静动态性能测试系统研究[J]．中国工程机械学报．2010，vol.8(1)：91-95．
[75]董清，高曙，鲍海．同步发电机调速系统附加仉鲁棒分散控制．中国电机工程学报，2002，22(H2)2：2．3
[76]王进华．混合H∞鲁棒控制理论及应用研究：[西北工业大学博士论]西安：西北大学，2000，51—53
[77]姚波，王福忠，张庆灵，基于LMI可靠跟踪控制器设计，自动化学报，30(6)：863—871，2004。
[78] R Lu，H．Su and J．Chu，RobustH∞control for a class of uncertain Lur’e singular
[79] systems with time-delays, Proceedings of the42nd IEEE Conferences On Decision andControl, Maui．Hawaii,USA,5585-5590．2003．
[80]嵇小辅，不确定线性系统鲁棒控制若干问题研究，浙江大学博士学位论文，2006。
[81]郑大钟．线性系统理论[M]．北京：清华大学出版社，2005
[82]张言俊，张科．系统辨识理论及应用[M]．北京：国防工业出版社，2004
[83]莫建林，王伟，许晓鸣，张卫东．系统辨识中的闭环问题[J]．控制理论与应用，2002，4(9)：15一16
[84]潘立登，潘仰东．系统辨识与建模[M]．北京：化学工业出版社，2004.
[85]行娟娟．轧机液压AGC系统数学模型及其控制方法研究[D].西安理工大学，2004
[86]李志宏．神经网络鲁棒控制在轧机厚度控制系统中的应用[D]．燕山大学，2001
[87]檀国节．辨识时变随机系统遗忘因子算法的收敛性分析．控制理论与应用，1999，16(6)：928-930
[88]孔祥东，单东升，董国江等．板厚自动控制(HAGC)系统建模与仿真研究．数字制造科学，2003，1(1-4)：256-276
[89]朱学彪．2800轧机液压系统在线监测与故障诊断，武汉科技大学硕士学位论文，2005
[90]朱学彪，陈奎生，付连东．基于AMFC的电液伺服系统控制算法研究.液压气动与密封，2008.4
[91]刘福才．非线性系统的模糊模型辨识及其应用．北京：国防工业出版社，2006
[92] Thomas Stuezle,Hoos Holger H. MAX-MIN Ant System.Future Generation ComputerSystems，2000，16(8)：889～914
[93]刘长良，于希宁，姚万业，刘吉臻．基于遗传算法的火电厂热工过程模型辨识[J]．中国电机工程学报，2003，23(3)：170～174
[94]孙一康．带钢热连轧的模型与控制[M]．北京：冶金工业出版社．2002
[95]陈永进，王一晶，慈春令．H∞控制模型匹配问题的一种解法[J]．燕山大学学报，2001，25：37．40
[96]吉明，姚储梁．鲁棒控制系统[M]．哈尔滨：哈尔滨工程大学出版社，2002
[97] B．Xian，M．S．de Queiroz，D．M．Dawson and I．Walker,Task-Space TrackingControl of Robot Manipulators via Quatemion Feedback． IEEE． Transaction onRobotics and Automation，2001，20(1)：265—272．
[98]张立群，邵惠鹤．面向控制器设计的多变鹫系统辨识实验信号[J]．控制与决策，2004，19(7)：824—830
[99]任子武，伞冶．自适应遗传算法的改进及在系统辨识中应用研究[J]．系统仿真学报，2006，18(1)：4l-43
[100]姚莉．多变龄模型测试信号设计与辨识[D]．杭州：浙江人学，2006：2-6
[101]杨刚，孙健国，李秋红，航空发动机控制系统中的增广LQR方法，航空动力学报， Vol.19, No.1, Feb.2004
[102]李旭，高升，熊忠辉．一类多变量系统特殊矩形波响应在线辨识[J]．动力工程，2004，24(1)：95—97
[103]全亚斌，张卫东，许晓鸣．二阶加延时模型的阶跃响应辨识方法唧．控制理论与应用，2002，19(6)：954-956
[104]朱六璋．不确定性系统定性建模与控制研究，中国科学技术大学博士论文.2002．
[105]孙多青，不确定系统的模糊自适应控制与辨识新方法研究，北京航空航天大学博士论文，2002．1．
[106] Daniel E. M., etc., Simultaneous Stabilization with Near Optimal LQR Performance, IEEETrans. Automatic Control, Vol.46, No.10, Oct.2001
[107] Katsuhiko Ogata(美)，Modern Control Engineering(3).北京：电子工业出版社，2000
[108]刘华平，孙富春，何克忠，孙增沂．奇异摄动控制系统：理论与应用．控制理论与应用，2003，20(1)：1-7．
[109]谢湘生，刘洪伟．滞后广义系统反馈镇定控制器设计的LMI方法[JJ．系统工程与电子技术．2000，22(11)：35-38．
[110]张先明，吴敏，何勇．线性时滞广义系统的时滞相关H∞控制．控制理论与应用，2005，22(4)649-652．
[2] Gevers M. Connecting Identification and Robust Control[J]. The Modeling of Uncertaintyin Control Systems,1994,192:35~37.
[3]冯旭，孙优贤等.鲁棒辨识问题评述[J].控制理论与应用,1993,10(6):609~616.
[4] Ljung L. Stable Identification: Theory for the Users[M]. New Jersey: Prentice-Hall Inc.,1987.
[5] Fogel E. System Identification via Membership Set Constraints with Energy ConstrainedNoise [J]. IEEE Transactions on Automatic Control,1979,24(5):752~758.
[6]王文正，蔡金狮.飞行器气动参数的集员辨识[J].宇航学报,1998,19(2):31~36.
[7]徐长江，宋文忠.基于小波变换的分频集员辨识[J].控制与决策,1999,14(1):48~52.
[8]柴伟，孙先仿.一种非线性系统集员辨识算法[J].北京航空航天大学学报,2005,31(11):1237~1244.
[9]王晓军，邱志平.线性时不变系统集员辨识的区间算法[J].力学学报,2005,37(6):713~718.
[10] Alamo T, Bravo J M, Redondo M J. A Set Membership State Estimation Algorithm Basedon DC Function[J]. Automatica,2008,44(1):216~224.
[11]和丽清，孙先仿，邱红专.非线性系统的结构选择及其参数的集员辨识[J].北京航空航天大学学报,2010,36(10):1189~1193.
[12] Reppa V, Vagia M, Tzes A. Fault Detection Using Set Membership Identification forMicro-Electrostatic Actuators[C].16th IEEE International Conference on ControlApplications, Piscataway,2007.
[13] Ingimundarson A, Bravo J M, Puig V. Robust Fault Diagnosis Using Parallelotope-BasedSet-Membership Consistency Tests[C]. Proceedings of the44th IEEE Conference onDecision and Control, Piscataway,2005.
[14] Chen J. Frequency Domain Tests for Validation of Linear Fractional Uncertain Model[J].IEEE Transactions on Automatic Control,1997,42(6):748~760.
[15] Parrilm P A, Sznaier M. Mixed Time/Frequency Domain Based Robust Identification[J].Automatica,1998,34(11):1375~1389.
[16] Helmicki J A, Jacobson C A, Nett C N. A Worst Case Deterministic Approach in H1[J].IEEE Transactions on Automatic Control,1999,6(10):1163~1176.
[17] Gu G X, Khargonekar P P. A Class of Algorithms for Identification in H∞[J]. Automatica,1992,28:299~312.
[18] Bai E W, Raman S. Robust System Identification with Noisy Experimental DataProjection Operator and Linear Algorithms[J]. Automatica,1994,30(7):1203~1206.
[19] Zoltan S, Jozsef B, Ferenc S. Identification of Rational Approximate Models in H∞UsingGeneralized Orthonormal Basis[J]. IEEE Transactions on Automatic Control,1999,44(1):153~158.
[20] Xiong Y, Peng J X, Ding M Y. Algorithm for Identification of Objects in a ComplexBackground[C]. Proceedings of SPIE International Society for Optical Engineering, SanDiego,1995.
[21] Akcay H. General Orthonormal Basis for Robust Identification in H∞[C]. Proceedings ofthe41st IEEE Conference on Decision&Control, Nevada,2002.
[22] Mario T, Michele T. H∞Set Membership Identification: A Survey[C]. Proceedings of the43rd IEEE Conference on Decision&Control, Nassau,2004.
[23] Chen J, Nett N. Worst-Case System Identification in H∞: Validation of a Priori inFormation, Essentially Optimal Algorithms and Error Bounds[C]. Proceedings of theAmerican Control Conference, Chicago,1994.
[24] Gu G X, Xiong D P, Zhou K M. Identifi in H∞Using Pick′s Interpolation[J]. System&Control Letter,1993,20(4):263~272.
[25] Jacobson C A, Nett C N, Partington J R. Worst-Case System Identification in l1: OptimalAlgorithms and Error Bounds[J]. System&Control Letter,1992,19(6):419~424.
[26] Li S P. A Recursive Interpolatory Algorithm for Robust Identification[C]. Proceedings ofthe5th World Congress on Intelligent Control and Automation, Hangzhou,2004.
[27] Chen J, Gu G. Worst Case Asymptotic Properties of Identification[J]. IEEE Transactionson Circuits and Systems,2002,49(4):437~446.
[28]李昇平，方京华，黄心汉.最坏情况下l1鲁棒辨识研究[J].控制与决策,1995,10(3):40~46.
[29]李昇平，方京华，黄心汉. l1鲁棒辨识收敛性态的研究[J].控制与决策,1996,11(1):71~77.
[30]李昇平. l1鲁棒辨识:一种递推插值方法[J].控制理论与应用,2002,19(6):67~73.
[31] Pena R S, Galarza C G. Practical Issues in Robust Identification[J]. IEEE Transactions onControl System Technology,1994,2(1):54~56.
[32] Chen J, Nett C N. The Caratheodory-Fejer Problem and H∞/l1Identification: A TimeDomain Approach[C]. Proceedings of the32nd IEEE Conference on Decision&Control,San Antonio,1993.
[33] Tong Z, Kimura H. Structure of Model Uncertainty for a Weakly Corrupted Plant[J]. IEEETransactions on Automatic Control,1995,40(4):639~655.
[34] Sanchez P, Sznaier M. Robust Identification with Mixed Time/Frequency Experiments:Consistency and Interpolation Algorithms[C]. Proceedings of the IEEE Conference onDecision&Control, Taejon,1995.
[35] Parrilo P A, Sznaier M, Sanchez P. Robust Mixed Time/Frequency Domain Based RobustIdentification[C]. Proceedings of the IEEE Conference on Decision&Control, Kobe,1996.
[36] Gu G X, Chen J. A Nearly Interpolatory Algorithm for H∞Identification with Mixed Timeand Frequency Response Data[J]. IEEE Transactions on Automatic Control,2001,46:464~469.
[37]窦立谦，宗群，刘文静.面向控制的系统辨识研究进展[J].系统工程与电子技术,2009,31(1):158~164.
[38] Davison E J. The Output Control of Linear Time Invariant Multivariable System withUnmeasurable Arbitrary Disturbances[J]. IEEE Transactions on Automatic Control,1972,17(5):621~630.
[39] Doyle J C, Stein G. Multivariable Feedback Design: Concepts for a Classical/ModernSynthesis[J]. IEEE Transactions on Automatic Control,1981,26(1):4~16.
[40] Packard A, Doyle J C. The Complex Structured Singular Value[J]. Automatica,1993,29(1):71~109.
[41]梅生伟，申铁龙，刘康志.现代鲁棒控制理论与应用[M].北京:清华大学出版社,2003.
[42]李新国，毛承元，陈红英. H∞制导律统计性能分析[J].西北工业大学学报,2004,1:27~30.
[43]查旭，崔平远，刘永才.导弹纵向机动鲁棒控制[J].航天控制,2003,4:44~48.
[44]李友年，贾晓红，王海波. H∞控制理论在空空导弹自动驾驶仪设计中的应用[J].航空兵器,2004,6:13~15.
[45] Zhang Y, Wu J G, Xu Y. The Application of H∞/Mixed Sensitivity Approach to AttitudeControl System for a Winged Missile[C]. Proceedings of the5th World Congress onIntelligent Control and Automation, Hangzhou,2004.
[46]张羽飞，冯汝鹏.巡航导弹成像器稳定装置H2/H∞混合最优PID控制器设计[J].航天控制,2003,3:8~13.
[47]姜世洲，洪华杰，纪明.基于QFT的光电稳定控制系统设计与分析[J].应用光学,2009,30(3):377~381.
[48] Haessing D, Decotiis J, Modern Control Methods Applied to a Line-of-Sight Stabilizationand Tracking System[C]. Proceedings of the American Control Conference, New York,1987.
[49]唐京珊.定量反馈理论及其在卫星姿态控制系统中的应用[D].哈尔滨,哈尔滨工业大学,2003.
[50]张庆振. QFT/TECS在飞机自动着陆控制中的应用研究[D].西安,西北工业大学,2004.
[51]晋严尊，杨一栋.空空导弹自适应飞控系统的定量反馈理论设计[J].南京航空航天大学学报,2001,33(1):86~90.
[52]刘小明.基于定量反馈理论的天线稳定平台跟踪控制系统设计[J].电光与控制,2009,16(10):83~86.
[53]宋勇，邓辉，张宗麟.定量反馈理论在侧向飞行控制系统中的应用[J].火力与指挥控制,2007,32(3):72~75.
[54]张福星，朱荣，熊威.基于定量反馈理论的微型飞行器增稳控制器[J].清华大学学报(自然科学版),2010,50(2):219~223.
[55] Arimoto S, Miyazaki F. Bettering operation of robotics by learning [J], J.Robotic System,1984,1(2):123~140.
[56] Hwang D H, Bien Z, Oh S R. Iterative learning control method for discreteeime dynamicsystems [C]. IEE Proceedings,1991,138(2):139~144.
[57] Tomohide N, Arimoto S. Learning control for robot tasks under geometric endpointconstraints [J]. IEEE Trans. On Robotics and Automation,1995,11(3):432~44.
[58]谢胜利，谢振东，韦岗等.非线性分布参数系统跟踪控制的学习算法[J].自动化学报，1999，25(5)：635~640.
[59] Yong F, Chow T W.2-D analysis for iterative learning controller for discrete-time systemswith variable initial conditions[J]. IEEE Trans.on Circuits and System I: FundamentalTheory and Applications,2003,50(5):722-727.
[60]孙涛.中厚板高精度厚度控制的研究与应用[D].沈阳,东北大学,2009.
[61]王小英.带钢热连轧精轧机组自动厚度控制系统研究[D].上海,上海交通大学,2008.
[62]郭迪. HC轧机的自动厚度控制[D].武汉,华中科技大学,2006.
[63]王国栋，刘相华，王军生.冷连轧生产工艺的进展[J].轧钢,2003,20(1):37~41.
[64] Bristow D A, Marina T, Andrew G A. A survey of iterative learning control: A learning-based method forhigh-performance tracking control [J]. IEEE Control Systems Magazine,2006,6(3):96–114.
[65] Chin I, Qin S J, Lee K S. A two-stage iterative learning control technique combined withreal-time feedback for independent disturbance rejection [J]. Automatica,2004,40(11):1913–1920.
[66] Chen C K, James H W. PD-type iterative learning control for trajectory tracking of apneumatic X-Y table with disturbances[C]. Proceedings of the2004IEEE InternationalConference on Robotics and Automation,2004,30(6):3500–3505.
[67] Levoci P A, Longman R W. Frequency domain prediction of final error due to noise inlearning and repetitive control [J]. Advances in the Astronautical Sciences,2002,112(2):1341–1359.
[68] Chen Y Q, Moore K L. A practical iterative learning path-following control of an omni-directional vehicle [J]. Asian Journal of Control,2002,4(1):90–98.
[69]陈奎生．液压与气压传动[M]．北京：机械工业出版社，1995
[70]湛从昌，傅连东，陈新元．液压可靠性与故障诊断[M]．北京：冶金工业出版社，2009．
[71]傅连东，朱学彪，李金良等．伺服缸测试系统的设计[J]．液压与气动，2006.01：30-32．
[72]王春行．液压控制系统[M]．北京：机械工业出版社，1995
[73]张莉松，胡祐德，徐立新．伺服系统原理与设计[M]．北京：北京理工大学出版社，2006．
[74]付曙光，陈奎生，湛从昌等．伺服液压缸静动态性能测试系统研究[J]．中国工程机械学报．2010，vol.8(1)：91-95．
[75]董清，高曙，鲍海．同步发电机调速系统附加仉鲁棒分散控制．中国电机工程学报，2002，22(H2)2：2．3
[76]王进华．混合H∞鲁棒控制理论及应用研究：[西北工业大学博士论]西安：西北大学，2000，51—53
[77]姚波，王福忠，张庆灵，基于LMI可靠跟踪控制器设计，自动化学报，30(6)：863—871，2004。
[78] R Lu，H．Su and J．Chu，RobustH∞control for a class of uncertain Lur’e singular
[79] systems with time-delays, Proceedings of the42nd IEEE Conferences On Decision andControl, Maui．Hawaii,USA,5585-5590．2003．
[80]嵇小辅，不确定线性系统鲁棒控制若干问题研究，浙江大学博士学位论文，2006。
[81]郑大钟．线性系统理论[M]．北京：清华大学出版社，2005
[82]张言俊，张科．系统辨识理论及应用[M]．北京：国防工业出版社，2004
[83]莫建林，王伟，许晓鸣，张卫东．系统辨识中的闭环问题[J]．控制理论与应用，2002，4(9)：15一16
[84]潘立登，潘仰东．系统辨识与建模[M]．北京：化学工业出版社，2004.
[85]行娟娟．轧机液压AGC系统数学模型及其控制方法研究[D].西安理工大学，2004
[86]李志宏．神经网络鲁棒控制在轧机厚度控制系统中的应用[D]．燕山大学，2001
[87]檀国节．辨识时变随机系统遗忘因子算法的收敛性分析．控制理论与应用，1999，16(6)：928-930
[88]孔祥东，单东升，董国江等．板厚自动控制(HAGC)系统建模与仿真研究．数字制造科学，2003，1(1-4)：256-276
[89]朱学彪．2800轧机液压系统在线监测与故障诊断，武汉科技大学硕士学位论文，2005
[90]朱学彪，陈奎生，付连东．基于AMFC的电液伺服系统控制算法研究.液压气动与密封，2008.4
[91]刘福才．非线性系统的模糊模型辨识及其应用．北京：国防工业出版社，2006
[92] Thomas Stuezle,Hoos Holger H. MAX-MIN Ant System.Future Generation ComputerSystems，2000，16(8)：889～914
[93]刘长良，于希宁，姚万业，刘吉臻．基于遗传算法的火电厂热工过程模型辨识[J]．中国电机工程学报，2003，23(3)：170～174
[94]孙一康．带钢热连轧的模型与控制[M]．北京：冶金工业出版社．2002
[95]陈永进，王一晶，慈春令．H∞控制模型匹配问题的一种解法[J]．燕山大学学报，2001，25：37．40
[96]吉明，姚储梁．鲁棒控制系统[M]．哈尔滨：哈尔滨工程大学出版社，2002
[97] B．Xian，M．S．de Queiroz，D．M．Dawson and I．Walker,Task-Space TrackingControl of Robot Manipulators via Quatemion Feedback． IEEE． Transaction onRobotics and Automation，2001，20(1)：265—272．
[98]张立群，邵惠鹤．面向控制器设计的多变鹫系统辨识实验信号[J]．控制与决策，2004，19(7)：824—830
[99]任子武，伞冶．自适应遗传算法的改进及在系统辨识中应用研究[J]．系统仿真学报，2006，18(1)：4l-43
[100]姚莉．多变龄模型测试信号设计与辨识[D]．杭州：浙江人学，2006：2-6
[101]杨刚，孙健国，李秋红，航空发动机控制系统中的增广LQR方法，航空动力学报， Vol.19, No.1, Feb.2004
[102]李旭，高升，熊忠辉．一类多变量系统特殊矩形波响应在线辨识[J]．动力工程，2004，24(1)：95—97
[103]全亚斌，张卫东，许晓鸣．二阶加延时模型的阶跃响应辨识方法唧．控制理论与应用，2002，19(6)：954-956
[104]朱六璋．不确定性系统定性建模与控制研究，中国科学技术大学博士论文.2002．
[105]孙多青，不确定系统的模糊自适应控制与辨识新方法研究，北京航空航天大学博士论文，2002．1．
[106] Daniel E. M., etc., Simultaneous Stabilization with Near Optimal LQR Performance, IEEETrans. Automatic Control, Vol.46, No.10, Oct.2001
[107] Katsuhiko Ogata(美)，Modern Control Engineering(3).北京：电子工业出版社，2000
[108]刘华平，孙富春，何克忠，孙增沂．奇异摄动控制系统：理论与应用．控制理论与应用，2003，20(1)：1-7．
[109]谢湘生，刘洪伟．滞后广义系统反馈镇定控制器设计的LMI方法[JJ．系统工程与电子技术．2000，22(11)：35-38．
[110]张先明，吴敏，何勇．线性时滞广义系统的时滞相关H∞控制．控制理论与应用，2005，22(4)649-652．