卫星姿态控制系统的故障诊断与容错方法研究
|
摘要
姿态控制系统是保障卫星在轨正常工作的核心部分，由于长期运行于恶劣的空间环境，尤其是敏感器、执行机构等部件长期不断地在执行各类控制操作，极易出现故障，进而会导致不可修复的严重后果，因此卫星姿态控制系统的故障诊断和容错控制技术一直是航天领域研究热点和难点问题。同时，由于机构运动、燃料消耗及空间环境影响等将导致在轨卫星的系统模型参数发生未知变化，传统上基于精确模型的姿态容错控制技术具有一定的局限性。本论文在国家自然科学基金项目(课题编号：61104026)资助下，面向我国重大设施及装备的高可靠性及长寿命战略发展需求，从理论和应用两方面对具有不确定模型影响的卫星姿态容错控制问题进行了深入的研究，取得如下研究成果。
考虑存在各种噪声、未知干扰和模型不确定性，引入鲁棒H∞控制理论，提出了一种面向线性不确定系统的完整性被动容错控制设计方法。该方法利用线性不确定系统模型，将执行机构/姿态敏感器失效情况下被动容错控制器设计转换为相应的线性矩阵不等式的可行性问题，并进一步给出了系统具有完整性的充分条件和鲁棒H∞容错控制器的设计方法。在对卫星姿态动力学模型线性化基础上，采用所提方法设计了执行机构/姿态敏感器故障状态下的卫星姿态鲁棒H∞容错控制器，数学仿真分析表明该控制器能够保证执行机构/姿态敏感器突变或缓变故障时卫星姿态控制系统的控制性能，具有对已知故障良好的容错能力。
针对存在未知干扰输入的Lipschitz非线性系统，融合基于模型和数据驱动的控制技术，提出了一种基于迭代学习未知输入观测器(IL-UIO)的主动容错控制方法。首先，采用对模型不确定和干扰解耦的原理，设计了一种非线性未知输入观测器，增强了系统故障观测的鲁棒性；在此基础上，利用被控系统的I/O数据，采用迭代学习利用上一步的故障观测信息来估计当前故障信息，跟踪和检测系统由于故障引起的改变并进而实现对故障的在线重构。采用Lyapunov稳定性理论证明了IL-UIO的鲁棒稳定和速度误差的一致有界性。将所提方法应用于面向执行机构故障的卫星姿态容错控制器设计，数学仿真结果表明IL-UIO能够有效跟随过去的故障系统，确保一定系统性能的前提下实现对飞轮故障的容错，由于采用了基于模型的观测器与数据驱动方法相结合，二者相互渗透、优势互补，即可增强系统残差对未知干扰输入的鲁棒性，又提高了系统对数据的使用质量。
由于卫星姿态敏感器故障会污染到下一时刻其它健康敏感器的测量信息，影响对真正故障源的准确判断，对此，考虑卫星动力学模型参数发生的未知变化，基于PCA和SIM识别技术，提出了一种基于子空间数据驱动的卫星姿态敏感器故障检测、隔离和辨识(FDI)方法。该方法首先采用等价空间法对测量数据进行预处理，构建残差信号；然后采用子空间法辨识出增广观测矩阵，进而实现敏感器故障鲁棒检测；并通过线性变换对等价空间进行降阶，减小算法在线计算量。最后，将上述方法应用于卫星姿控系统的敏感器故障问题，数学仿真结果表明，不依赖故障先验知识，仅利用输入输出观测数据，就能够在线完成未知系统模型情况下的敏感器故障检测、隔离与辨识，由于采用了故障降阶检测处理，有效减少了计算量，适宜于卫星在轨应用。
状态监测和系统重构是卫星长期自主运行的保障，也是开发星上健康管理系统的关键技术之一，首先，给出了一种含有状态监测和系统重构环节的健康管理系统容错控制体系结构；针对存在未知干扰输入的非线性系统，采用基于数据驱动的模式识别方法，设计了一组基于支持向量机(SVM)的非线性故障估计器，利用正常操作条件下和各种故障状态下的历史数据实现了故障信息的近似检测和隔离估计；进而利用检测的系统信息和辨识出的系统参数，设计了学习观测器，通过学习算法提供用于控制器重构的信息，修改反馈控制律，实现了对故障的自适应故障补偿，并理论证明了故障估计误差有界。最后，以卫星对地观测过程中自主运行任务为例进行了仿真验证，结果表明在执行器发生未预知故障时仍可以实现系统状态监测和故障重构。
Attitude control system is the core subsystem to maintain regular satellite operation.Due to long-term in-orbit work in the harsh environment of space, the components suchas actuators and sensors in the attitude control system continue to perform in-orbitcontrol operations, therefore are prone to faults. Due to the severe consequence of faultirreparability, the satellite attitude control system fault diagnosis and fault tolerant controltechnology has always been a strong concern in the field of aerospace industry.Meanwhile, conventional fault-tolerant attitude control techniques based on precisemodels have limitations to deal with the unknown changes of on-orbit satellite systemmodel parameters caused by mechanical motion, fuel consumption and spaceenvironment. Oriented from the high reliability and long-life requirement for majorfacilities and equipment, and funded by the National Natural Science Foundation ofChina (61104026), the thesis performs in-depth study on fault-tolerant control issues ofsatellite attitude control system with model uncertainties in both theoretical and appliedaspects. The research mainly involves the following points.
Considering the existence of noises, unknown disturbances and model uncertainties,an integrity passive fault-tolerant control design method based on robust H∞theory isproposed to deal with the linear uncertain system control problem. It uses linear uncertainsystem model to transform the passive fault-tolerant control problem into the feasibilityof corresponding linear matrix inequalities in the case of actuator/sensor failure. Inaddition, it gives the sufficient condition for system completeness, and fault-tolerantcontroller design method using robust H∞theory. Based on linearized model of satelliteattitude dynamics model, the fault-tolerant robust H∞controller is designed in the case ofactuator/attitude sensor fault states. Mathematical simulation analysis shows that thecontroller can guarantee control performance of satellite attitude control system againstfaults of sudden or slow actuator/attitude sensor changes, therefore have good fault-tolerant performance against known faults.
To deal with Lipschitz nonlinear system with unknown disturbance input, an activefault-tolerant control method based on iterative learning-unknown input observer (IL-UIO) is proposed. First, using the principle of decoupling model uncertainties anddisturbance, a nonlinear unknown input observer is designed to guarantee robustness forfault observation. Then based on I/O data of the controlled system, the current faultinformation is estimated with last step fault observation using IL. The estimation is usedto track and detect fault caused system changes, and implement online faultreconstruction. The robust stability and uniform boundness of IL-UIO are proven usingLyapunov stability theory. The proposed method is applied to the design of fault-tolerant satellite attitude controller with actuator faults. Mathematical simulation results showthat IL-UIO can effectively track past fault system and realize tolerance against flywheelfaults with certain guaranteed system performance. The combination and fusion ofmodel-based observer and data driven approach are adopted here to reinforce robustnessof system residual against unknown disturbance input and improve system data usequality.
Satellite attitude sensor faults may pollute measurement information of otherhealthy sensors at next time step, and therefore affect accurate determination of the realfault source. To address the problem and unknown changes of satellite dynamics modelparameters, a fault detection, isolation and identification (FDI) method is proposed,which is based on subspace-aided data driven design using PCA and SIM identificationtechniques. First the equivalent space method is used to preprocess measured data andconstruct residual signal. Then the subspace method is employed to identify augmentedobservation matrix to realize robust detection of sensor faults. The order of the equivalentspace is reduced further with linear transformation to reduce online computation. Finally,the proposed method is applied to satellite attitude control problem with sensor faults.Mathematical simulation results show that it can achieve online FDI with unknownsystem model by only using input-output observation, independent on prior faultknowledge. The adoption of reduced-order fault detection and processing effectivelyreduces computation, and makes the method suitable for online satellite applications.
State monitoring and system reconfiguration safeguard long-term autonomoussatellite running, and are also key techniques in the development of onboard healthmanagement system. First, a fault-tolerant control architecture of health managementsystem containing state monitoring and system reconfiguration is given. Consideringnonlinear system with unknown disturbance input, the data driven based patternrecognition method is used to design a set of nonlinear fault estimators based on supportvector machine (SVM). It employs historical data in normal operation and faults states ofdifferent kind to achieve approximate detection and isolated estimation of faultinformation. The information and identified system parameters are then used to designlearning observer to realized adaptive fault compensation. The learning algorithm here isexploited to supply controller reconfiguration information and modify feedback controllaw. Theoretical analysis proves error boundness of fault estimation. Finally, simulationdemonstration is performed under the background of autonomous running task of smallsatellite earth observation. Results show system state monitoring and systemreconfiguration can be achieved in the case of unknown actuator faults.
考虑存在各种噪声、未知干扰和模型不确定性，引入鲁棒H∞控制理论，提出了一种面向线性不确定系统的完整性被动容错控制设计方法。该方法利用线性不确定系统模型，将执行机构/姿态敏感器失效情况下被动容错控制器设计转换为相应的线性矩阵不等式的可行性问题，并进一步给出了系统具有完整性的充分条件和鲁棒H∞容错控制器的设计方法。在对卫星姿态动力学模型线性化基础上，采用所提方法设计了执行机构/姿态敏感器故障状态下的卫星姿态鲁棒H∞容错控制器，数学仿真分析表明该控制器能够保证执行机构/姿态敏感器突变或缓变故障时卫星姿态控制系统的控制性能，具有对已知故障良好的容错能力。
针对存在未知干扰输入的Lipschitz非线性系统，融合基于模型和数据驱动的控制技术，提出了一种基于迭代学习未知输入观测器(IL-UIO)的主动容错控制方法。首先，采用对模型不确定和干扰解耦的原理，设计了一种非线性未知输入观测器，增强了系统故障观测的鲁棒性；在此基础上，利用被控系统的I/O数据，采用迭代学习利用上一步的故障观测信息来估计当前故障信息，跟踪和检测系统由于故障引起的改变并进而实现对故障的在线重构。采用Lyapunov稳定性理论证明了IL-UIO的鲁棒稳定和速度误差的一致有界性。将所提方法应用于面向执行机构故障的卫星姿态容错控制器设计，数学仿真结果表明IL-UIO能够有效跟随过去的故障系统，确保一定系统性能的前提下实现对飞轮故障的容错，由于采用了基于模型的观测器与数据驱动方法相结合，二者相互渗透、优势互补，即可增强系统残差对未知干扰输入的鲁棒性，又提高了系统对数据的使用质量。
由于卫星姿态敏感器故障会污染到下一时刻其它健康敏感器的测量信息，影响对真正故障源的准确判断，对此，考虑卫星动力学模型参数发生的未知变化，基于PCA和SIM识别技术，提出了一种基于子空间数据驱动的卫星姿态敏感器故障检测、隔离和辨识(FDI)方法。该方法首先采用等价空间法对测量数据进行预处理，构建残差信号；然后采用子空间法辨识出增广观测矩阵，进而实现敏感器故障鲁棒检测；并通过线性变换对等价空间进行降阶，减小算法在线计算量。最后，将上述方法应用于卫星姿控系统的敏感器故障问题，数学仿真结果表明，不依赖故障先验知识，仅利用输入输出观测数据，就能够在线完成未知系统模型情况下的敏感器故障检测、隔离与辨识，由于采用了故障降阶检测处理，有效减少了计算量，适宜于卫星在轨应用。
状态监测和系统重构是卫星长期自主运行的保障，也是开发星上健康管理系统的关键技术之一，首先，给出了一种含有状态监测和系统重构环节的健康管理系统容错控制体系结构；针对存在未知干扰输入的非线性系统，采用基于数据驱动的模式识别方法，设计了一组基于支持向量机(SVM)的非线性故障估计器，利用正常操作条件下和各种故障状态下的历史数据实现了故障信息的近似检测和隔离估计；进而利用检测的系统信息和辨识出的系统参数，设计了学习观测器，通过学习算法提供用于控制器重构的信息，修改反馈控制律，实现了对故障的自适应故障补偿，并理论证明了故障估计误差有界。最后，以卫星对地观测过程中自主运行任务为例进行了仿真验证，结果表明在执行器发生未预知故障时仍可以实现系统状态监测和故障重构。
Attitude control system is the core subsystem to maintain regular satellite operation.Due to long-term in-orbit work in the harsh environment of space, the components suchas actuators and sensors in the attitude control system continue to perform in-orbitcontrol operations, therefore are prone to faults. Due to the severe consequence of faultirreparability, the satellite attitude control system fault diagnosis and fault tolerant controltechnology has always been a strong concern in the field of aerospace industry.Meanwhile, conventional fault-tolerant attitude control techniques based on precisemodels have limitations to deal with the unknown changes of on-orbit satellite systemmodel parameters caused by mechanical motion, fuel consumption and spaceenvironment. Oriented from the high reliability and long-life requirement for majorfacilities and equipment, and funded by the National Natural Science Foundation ofChina (61104026), the thesis performs in-depth study on fault-tolerant control issues ofsatellite attitude control system with model uncertainties in both theoretical and appliedaspects. The research mainly involves the following points.
Considering the existence of noises, unknown disturbances and model uncertainties,an integrity passive fault-tolerant control design method based on robust H∞theory isproposed to deal with the linear uncertain system control problem. It uses linear uncertainsystem model to transform the passive fault-tolerant control problem into the feasibilityof corresponding linear matrix inequalities in the case of actuator/sensor failure. Inaddition, it gives the sufficient condition for system completeness, and fault-tolerantcontroller design method using robust H∞theory. Based on linearized model of satelliteattitude dynamics model, the fault-tolerant robust H∞controller is designed in the case ofactuator/attitude sensor fault states. Mathematical simulation analysis shows that thecontroller can guarantee control performance of satellite attitude control system againstfaults of sudden or slow actuator/attitude sensor changes, therefore have good fault-tolerant performance against known faults.
To deal with Lipschitz nonlinear system with unknown disturbance input, an activefault-tolerant control method based on iterative learning-unknown input observer (IL-UIO) is proposed. First, using the principle of decoupling model uncertainties anddisturbance, a nonlinear unknown input observer is designed to guarantee robustness forfault observation. Then based on I/O data of the controlled system, the current faultinformation is estimated with last step fault observation using IL. The estimation is usedto track and detect fault caused system changes, and implement online faultreconstruction. The robust stability and uniform boundness of IL-UIO are proven usingLyapunov stability theory. The proposed method is applied to the design of fault-tolerant satellite attitude controller with actuator faults. Mathematical simulation results showthat IL-UIO can effectively track past fault system and realize tolerance against flywheelfaults with certain guaranteed system performance. The combination and fusion ofmodel-based observer and data driven approach are adopted here to reinforce robustnessof system residual against unknown disturbance input and improve system data usequality.
Satellite attitude sensor faults may pollute measurement information of otherhealthy sensors at next time step, and therefore affect accurate determination of the realfault source. To address the problem and unknown changes of satellite dynamics modelparameters, a fault detection, isolation and identification (FDI) method is proposed,which is based on subspace-aided data driven design using PCA and SIM identificationtechniques. First the equivalent space method is used to preprocess measured data andconstruct residual signal. Then the subspace method is employed to identify augmentedobservation matrix to realize robust detection of sensor faults. The order of the equivalentspace is reduced further with linear transformation to reduce online computation. Finally,the proposed method is applied to satellite attitude control problem with sensor faults.Mathematical simulation results show that it can achieve online FDI with unknownsystem model by only using input-output observation, independent on prior faultknowledge. The adoption of reduced-order fault detection and processing effectivelyreduces computation, and makes the method suitable for online satellite applications.
State monitoring and system reconfiguration safeguard long-term autonomoussatellite running, and are also key techniques in the development of onboard healthmanagement system. First, a fault-tolerant control architecture of health managementsystem containing state monitoring and system reconfiguration is given. Consideringnonlinear system with unknown disturbance input, the data driven based patternrecognition method is used to design a set of nonlinear fault estimators based on supportvector machine (SVM). It employs historical data in normal operation and faults states ofdifferent kind to achieve approximate detection and isolated estimation of faultinformation. The information and identified system parameters are then used to designlearning observer to realized adaptive fault compensation. The learning algorithm here isexploited to supply controller reconfiguration information and modify feedback controllaw. Theoretical analysis proves error boundness of fault estimation. Finally, simulationdemonstration is performed under the background of autonomous running task of smallsatellite earth observation. Results show system state monitoring and systemreconfiguration can be achieved in the case of unknown actuator faults.
引文
[1]谭春林,胡太彬,王大鹏,等.国外航天器在轨故障统计与分析[J].航天器工程,2011,20(4):130-136.
[2] Gelderloss H, Young D. Redundancy management of shuttle flight control rategyroscopes and accelerometers[C]. Proceedings of the1982American ControlConference, Arlington, VA, USA,1982:808-811.
[3] Eryurek E, Upadhyaya B R. Fault tolerant control and diagnostic for large scalesystems[J]. IEEE Control Systems Magazine,1995,15(5):34-42.
[4] Garcia H E, Ray A, Edwards R M. Reconfigurable control of power plants usingautomata[J]. IEEE Control Systems Magazine,1991,11(11):85-92.
[5] Celi R, Huang C Y, Shih I C. Reconfigurable flight control for a tandem rotorhelicopter[C].52nd Annual National Forum of the American Helicopter Society,Washington, DC, USA,1996:1569-1588.
[6] Huang C Y, Stengel R F. Restructurable control using proportional-integral implicitmodel following[J]. Journal of Guidance, Control and Dynamics,1990,13(2):303-309.
[7] Mayback P S, Stevens R D. Reconfigurable flight control via multiple modeladaptive control methods[J]. IEEE Transactions on Aerospace and ElectronicSystems,1991,27(2):470-479.
[8] Morse W D, Ossman K A. Model following reconfigurable flight control system forthe AFTI/F-16[J]. Journal of Guidance, Control and Dynamics,1990,13(6):969-976.
[9] Samedov R. Approach to the support of the fault tolerance of the double redundantcomputer control systems[J]. Mathematical and Computational Applications,1999:175-180.
[10] Kobayashi Y, Ikeda M, Fujisaki Y. Fault-tolerant decentralized control for largespace structures[C]. Proceedings of the38th IEEE Conference on Decision andControl, Phoenix, AZ, USA,1999:303-308.
[11] Yeung W H R, Moore P R. Genetic algorithms and flexible process planning in thedesign of fault tolerant cell control for flexible assembly systems[J]. InternationalJournal of Computer Integrated Manufacturing,2000,13(2):157-161.
[12] Rauch H E. Autonomous control reconfiguration[J]. IEEE Control SystemsMagazine,1995,15(6):37-48.
[13] Burkholder J O. Adaptive Algorithms for Dynamic Systems Diagnostics and Fault-Tolerant Control[D]. Virginia: The University of Virginia,2010:1-3.
[14] Mack S, Tao G., Burkholder J O. An adaptive detection scheme for aircraftaerodynamic system damages[C].2010American Control Conference, VA, USA,2010:542-547.
[15] Henry D. Fault diagnosis of Microscope satellite thrusters using h∞/h filters[J].AIAA Journal of Guidance Control and Dynamics,2008,31(3):699–711.
[16] Edwards C, Lombaerts T, Smaili H. Fault Tolerant Flight Control A BenchmarkChallenge[M]. Berlin: Springer-Verlag,2010:131-140.
[17] Patton R J, Uppal F J, Simani S, et al. Robust FDI Applied to Thruster Faults of ASatellite System[J]. Control Engineering Practice,2010,18(9):1093-1109.
[18] Patton R J, Uppal F J, Simani S, et al. Reliable fault diagnosis scheme for aspacecraft attitude control system[J]. Journal of Risk and Reliability,2008,222(2):139-152.
[19] Beard R V. Failure Accommodation in Linear Systems through Self-reorganization[R]. Report MVT-71-1, MIT, Cambridge, Massachusetts,1971.
[20] Mehra R K, Peschon J. An Innovation Approach to Fault Detection and Diagnosisin Dynamics[J]. Automatica,1971,7:637-640.
[21] Willsky A S. A Survey of Design Methods for Failure Detection and Diagnosis inDynamics[J]. Automatica,1976,12:601-611.
[22] Himmelblau D M. Fault Detection and Diagnosis in Chemical and PetrochemicalProcess[M]. Amsterdam: Elsevier Press,1978,1-46.
[23]叶银忠,潘日芳,蒋慰孙.动态系统的故障检测与诊断方法[J].信息与控制,1985,15(6):27-34.
[24]周东华,孙优贤.控制系统的故障检测与诊断技术[M].北京:清华大学出版社,1994,1-24.
[25] Brown D W. A Prognostic Health Management Based Framework for Fault-Tolerant Control[D]. Atlanta: Georgia Institute of Technology,2011:33-40.
[26] Vachtsevanos G, Lewis F, Roemer M, et al. Intelligent fault diagnosis and prognosisfor engineering systems[M]. Hoboken: John Wiley&Sons,2007,172-279.
[27] Ding S X. Model-based Fault Diagnosis Techniques: Design Schemes, Algorithmsand Tools[M]. Berlin: Springer-Verlag,2008:1-10.
[28] Mahmoud M M, Jiang J, Zhang Y M. Active Fault Tolerant Control Systems:Stochastic Analysis and Synthesis[M]. Berlin: Springer-Verlag,2003:28-40.
[29] Jiang J, Yu X. Fault-tolerant control systems: A comparative study between activeand passive approaches[J]. Annual Reviews in Control,2012,36(1):60-70.
[30] Zhang Y M, Jiang J. Issues on integration of fault diagnosis and reconfigurablecontrol in active fault-tolerant control systems[C]. Proceedings of the6th IFACSymposium of Fault Detection Supervisionand Safety for Technical Processes,Beijing, China,2006:1513-1524.
[31] Benosman M. A survey of some recent results on nonlinear fault tolerant control[J].Mathematical Problems in Engineering, Hindawi Publishing Corporation,2009,vol.2010, paper586169.
[32] Saljak D D. Reliable Control Using Multiple Control Systems[J]. InternationalJournal of Control.1980,31:303-329.
[33] Vidyasagar M, Viswanadham N. Reliable Stabilization Using a multi-controllerconfiguration[J]. Automatica,1985,21(4):599-602.
[34] Gundes A N. Controller design for reliable stabilization[C]. Proceedings of12thIFAC World Congress,1993,4:1-4.
[35] Sebe N, Kitamori T. Reliable Stabilization Based on a Multi-CompensatorConfiguration[C]. Proceedings of12th IFAC World Congress,1993,4:5-8.
[36] Saeks R, Murray J. Fractional representation, algebraic geometry, and thesimultaneous stabilization problem[J]. IEEE Trans On Automatic Control,1982,24(4):895-903.
[37] Kabamba P T, Yang C. Simultaneous controller design for linear time-invariantsystems[J]. IEEE Trans On Automatic Control,1991,36(1):106-111.
[38] Olbrot A W. Fault tolerant control in the presence of noise: a new algorithm andsome open problems[C]. Proc. of12th IFAC World Congress,1993,7:467-470.
[39] Ackermann J. Robustness against Sensor Failures[J]. Automatica,1984,20(2):211-215.
[40] Morari M. Robust Stability of Systems with Integral Control[J]. IEEE Transactionson Automatic Control,1985,30:574-588.
[41] Huo Z H, Zheng Y, Xu C. A robust fault-tolerant control strategy for networkedcontrol systems[J]. Journal of Network and Computer Application,2011,34(2):708-714.
[42] Majid F, Amin N. Norm-bounded integrity conditions of uncertain multivariablelinear time-invariant systems under decentralized control with integral action[J].Journal of Process Control,2012,22(2):463-469.
[43]陈雪芹,耿云海,张迎春.基于LMI的鲁棒容错控制及其在卫星姿态控制中的应用[J].控制理论与应用,2008,25(1):95-99.
[44] Liang J J, Dong C Y, Wang Q. A Fault-tolerant Attitude Control System for aSatellite Based on Fuzzy Global Sliding Mode Control Algorithm[C].7thInternational Symposium on Instrumentation and Control Technology, Beijing,China,2008:127-128.
[45] Godard G, Kumar K D. Fault Tolerant Reconfigurable Satellite Formations UsingAdaptive Variable Structure Techniques[J]. Journal of Guidance, Control andDynamics,2010,33(3):969-984.
[46] Tudoroiu N, Khorasani K. Fault Detection and Diagnosis for Satellite's AttitudeControl System(ACS) Using an Interactive Multiple Model(IMM) Approach[C].IEEE Conference on Control Applications, Toronto, Canada, August28~31,2005:1287-1292.
[47]陈雪芹,张迎春,耿云海,等.基于IMM/EA的卫星姿态控制系统重构容错控制[J].系统工程与电子技术,2007,29(5):774-777.
[48] Fan L L, Song Y D. Model reference adaptive fault-tolerant control with applicationto wind turbines[C].2011Chinese Control and Decision Conference, Beijing,China,2011:1314-1319.
[49] Wang J, Rugh W J. Feedback Linearization Families for Nonlinear Systems[J].IEEE Transactions on Automatic Control,1987,32(10):935-940.
[50] Calise A J, Hovakimyan N, Idan M. Adaptive output feedback control of nonlinearsystems using neural networks[J]. Automatica,2001,37(8):1201-1211.
[51] Benjamin J O, Liu J F, David M P, et al. Data-based fault detection and isolationusing feedback control: Output feedback and optimality[J]. Chemical EngineeringScience,2009,64(10):2370-2383.
[52] Halim A, Chrisropher E, Andres M. Fault reconstruction using a LVP sliding modeobserver for a class of LVP systems[J]. Journal of the Franklin Institute,2012,349(2):510-530.
[53] Chrisropher E, Chee P T. Sensor fault tolerant control using sliding modeobservers[J]. Control Engineering Practice,2006,14(8):897-908.
[54] Varma S, Kumar K D. Fault tolerant satellite attitude control using solar radiationpressure based on nonlinear adaptive sliding mode[J]. Acta Astronautica,2010,66(3):486-500.
[55] Halim A, Chrisropher E. Fault tolerant control using sliding modes with on-linecontrol allocation[J]. Automatic,2008,44(7):1859-1866.
[56] Liao F, Lum K Y, Wang J L, et al. Adaptive control allocation for non-linearsystems with internal dynamics[J]. IET Control Theory and Application,2010,4(6):909-922.
[57] Andry A N, Shapiro E Y, Chung J C. Eigenstructure assignment for linearsystems[J]. IEEE Transactions on Aerospace Electronic Systems,1983,19(5):711-729.
[58] Cai G B, Hu C H, Duan G R. Eigenstructure assignment for linear parameter-varying systems with applications[J]. Mathematical and Computer Modelling,2011,53(5):861-870.
[59] Maciejowski J M. Predictive control with constraints[M]. Prentice Hall, EnglewoodCliffs,2002.
[60] Rodil S S, Fuente M J. Fault tolerance in the framework of support vector machinesbased model predictive control[J]. Engineering Applications of ArtificialIntelligence,2010,23(7):1127-1139.
[61] Anjali P D, Sachin C P, Shankar S N. Intelligent state estimation for fault tolerantnonlinear predictive control[J]. Journal of Process Control,2009,19(2):187-204.
[62] Verhaegen M, Verdult V. Filtering and systerm identification: an introduction[M].Cambridge University Press, Cambridge,2007.
[63] Wu Q, Saif M. Robust Fault Diagnosis for A Satellite Large Angle Attitude SystemUsing An Iterative Neuron PID(INPID) Observer[C]. In2006American ControlConference, New York,2006:5710-5715.
[64] Chen W, Saif M. Observer-based Fault Diagnosis of Satellite Systems Subject toTime-varying Thruster Faults[J]. Journal of Dynamic Systems Measurement andControl,2007,129(3):352-356.
[65] Hou Q, Cheng Y H, Lu N Y, et al. Study on FDD and FTC of Satellite AttitudeControl System Based on the Effectiveness Factor[C]. In2nd InternationalSymposium on Systems and Control in Aerospace and Astronautics, Shenzhen,China,2008:1096-1101.
[66] Jiang T, Khorasani K. A Fault Detection, Isolation and Reconstruction Strategy forA Satellite's Attitude Control Subsystem with Redundant Reaction Wheels[C]. InIEEE International Conference on Systems, Man and Cybernetics, Montreal, CookIslands,2007:1644-1650.
[67] Henry D. Fault diagnosis of Microscope Satellite Thrusters Using H-infinity/H-Filters[J]. Journal of Guidance, Control and Dynamics,2008,31(3):699-711.
[68] Talebi H A, Patel R V. An Intelligent Fault Detection and Recovery Scheme forReaction Wheel Actuator of Satellite Attitude Control Systems[C]. Proceedings ofthe2006IEEE International Conference on Control Applications,2006:2099-2104.
[69] Li Z Q, Ma L, Khorasani K. A Dynamic Neural Network-based Reaction WheelFault Diagnosis for Satellites[C]. In International Joint Conference on NeuralNetworks Vancouver, Canada,2006:3714-3721.
[70] Ji C W, Rong J J. Development of Fault Diagnosis System for Spacecraft Based onFault Tree and G2[J]. Journal of Beijing Institute of Technology,2002,11(4):444-448.
[71]侯忠生,许建新.数据驱动控制理论及方法的回顾和展望[J].自动化学报,2009,35(6):650-667.
[72] Uchiyama M. Formulation of high-speed motion pattern of a mechanical arm bytrial[J]. Transactions of the Society for Instrumentation and Control Engineering,1978,14(6):706-712.
[73] Arimoto S, Kawamura S, Miyazaki F. Bettering operation of robots by learning[J].Journal of Robotic Systems,1984,1(2):123-140.
[74] Ahn H S, Chen Y Q, Moore K L. Iterative learning control: brief survey andcategorization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews,2007,37(6):1099-1121.
[75] Hou Z S, Xu J X, Yan J W. An iterative learning approach for density control offreeway traffic flow via ramp metering[J]. Transportation Research, Part C:Emerging Technologies,2008,16(1):71-97.
[76] Ding S X, Zhang P, Naik A, et al. Subspace method aided data-driven design offault detection and isolation systems[J]. Journal of Process Control,2009,19(9):1496-1510.
[77] Lopes J A, Menezes J C. Multivariate monitoring of fermentation processes withnon-linear modelling methods[J]. Analytica Chimica Acta,2004,515(1):101-108.
[78] Patan K, Witczak M, Korbicz J. Towards robustness in neural network based faultdiagnosis[J]. International Journal of Applied Mathematics and Computer Sciences,2008,18(4):443-454.
[79] Fuessel D, Isermann R. Hierarchical motor diagnosis utilizing structural knowledgeand a self-learning neuro-fuzzy scheme[J]. IEEE Transactions on IndustrialElectronics,2000,47(5):1070-1077.
[80] Koppen-Seliger B, Frank P M. Fuzzy logic and neural networks in faultdetection[M]. Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms,New York: CRC Press,1999:169-209.
[81] Oza N C, Tumer I Y, Tumer K, et al. Classification of Aircraft Maneuvers for FaultDetection[C]. In Proceedings of Multiple Classifier Systems,2003:375–384.
[82] Park H, Mackey R, James M, et al. Analysis of Space Shuttle Main Engine DataUsing Beacon-based Exception Analysis for Multi-Missions[C]. In Proceedings ofthe IEEE Aerospace Conference, New York,2002,6:2835-2844.
[83] He F J, Shi W G. WPT-SVMs Based Approach for Fault Detection of Valves inReciprocating Pumps[C]. Proceedings of the American Control Conference,Anchorage, AK,2002:4566-4570.
[84] Schwabacher M. Machine Learning for Rocket Propulsion Health Monitoring[C].Proceedings of the SAE World Aerospace Congress, Dallas, TX,2005.
[85] Iverson D L. Inductive System Health Monitoring[C]. Proceedings of theInternational Conference on Artificial Intelligence, Las Vegas, USA,2004:1-7.
[86] Wegerich S W, Wilks A D, Pipke R M. Nonparametric modeling of vibration signalfeatures for equipment health monitoring[C]. Proceedings of IEEE AerospaceConference, New York,2003,7:3113–3121.
[87] Atlas L, Bloor G, Brotherton T, et al. An evolvable tri-reasoner IVHM system[C].Proceedings of the IEEE Aerospace Conference, New York,2001,6:3023-3037.
[88]章仁为.卫星轨道姿态动力学与控制[M].北京:北京航空航天大学出版社,1998:139-140.
[89] Barua A, Khorasani K. Hierarchical Fault Diagnosis and Health Monitoring inMulti-platform Space Systems[C]. IEEE of Aerospace conference,2009:1-13.
[90]于翠.飞轮控制系统的设计与实现[D].哈尔滨:哈尔滨工业大学硕士学位论文,2006:19-21.
[91]程颢.飞轮控制系统研究与设计[D].哈尔滨:哈尔滨工业大学博士学位论文,2009:96-109.
[92] Noura H, Theilliol D, Ponsart J C. Fault-tolerant Control Systems Design andPractical Applications[M]. Berlin: Springer-Verlag,2009.
[93]王小丽,倪茂林.基于自适应观测器的非线性系统故障诊断[J].空间控制技术与应用,2008,34(4):33-37.
[94]杨保华.航天器制导、导航与控制[M].北京:中国科学技术出版社,2011,276-286.
[95]田科丰,李智斌.基于自适应观测器的飞轮故障诊断方法[J].华中科技大学学报,2009,37(Sup. I):184-186.
[96]陈雪芹.集成故障诊断与容错控制研究及其在卫星姿态控制中的应用[D].哈尔滨:哈尔滨工业大学博士学位论文,2008:27-29.
[97]纪延琚.卫星姿态控制系统层次化故障诊断系统设计与实现[D].哈尔滨:哈尔滨工业大学硕士学位论文,2011:23-25.
[98]刘一武,陈义庆.星敏感器测量模型及其在卫星姿态确定系统中的应用[J].宇航学报,2003,24(2):162-167.
[99] Das R K, Sen S, Dasgupta S. Fault tolerant controller for attitude control of satellitelaunch vehicle via LMI approach[C]. Proceeding of the IEEE INDICON2004FirstIndia Annual Conference,2004:304-309.
[100] Ye S J, Zhang Y M, Wang X M, et al. An improved LMI approach for Static OutputFeedback Fault-Tolerant Control with application to flight tracking control[C].4thIEEE Conference on Industrial Electronics and Applications, Xi’an, China,2009:35-40.
[101] Ye S J, Zhang Y M, Rabbath C A, et al. An LMI approach to mixed H1/H∞robustfault-tolerant control design with uncertainties[C]. American Control Conference2009, St. Louis, MO,2009:5540-5545.
[102]张嗣瀛,高立群.现代控制理论[M].北京:清华大学出版社,2006:246-247.
[103]王德进. H2和H∞优化控制理论[M].哈尔滨:哈尔滨工业大学出版社,2001:72-111.
[104] Xie L H, De S, Carlos E. Robust H∞control for linear systems with norm-boundedtime-varying uncertainty[C]. IEEE Transactions on Automatic Control,1992,37(8pt I):1188-1191.
[105]陈雪芹,张迎春,耿云海,等.基于控制有效因子的卫星姿态控制系统在轨重构容错控制[J].宇航学报,2007,28(1):94-98.
[106]侯倩,程月华,姜斌,等.基于RO-NUIO/LMI的挠性卫星轨控期间姿控系统故障诊断[J].宇航学报,2011,32(10):2146-2151.
[107] Gao Z F, Jiang B, Shi P, et al. Sensor fault estimation and compensation formicrosatellite attitude control systems[J]. International Journal of ControlAutomation and Systems,2010,8(2):228-237.
[108]陈雪芹,耿云海,张世杰,等.基于混合H2/H∞的集成故障诊断与容错控制研究[J].宇航学报,2007,28(4):890-896.
[109] Hu Q L. Robust adaptive sliding-mode fault-tolerant control with L(2)-gainperformance for flexible spacecraft using redundant reaction wheels[J]. IETControl Theory and Applications,2010,4(6):1055-1070.
[110]张冬梅,孙明轩,俞立.基于观测器跟踪非一致轨迹的迭代学习控制器设计[J].控制理论与应用,2006,23(5):795-799.
[111] Chen W, Saif M. An iterative learning observer-based approach to fault detectionand accommodation in nonlinear systems[C]. Proceedings of the40th IEEEConference on Decision and Control, Orlando, USA,2001:4469-4474.
[112] Mondal S, Chakraborty G, Bhattacharyya K. LMI approach to robust unknowninput observer design for continuous systems with noise and uncertainties[J].International Journal of Control, Automation and Systems,2010,8(2):210-219.
[113] Wang D, Lum K Y. Adaptive unknown input observer approach for aircraft actuatorfault detection and isolation[J]. International Journal of Adaptive Control andSignal Processing,2007,21(1):31-48.
[114]肖冰,胡庆雷,马广富.基于观测器的航天器执行机构失效故障重构[J].宇航学报,2011,32(2):323-328.
[115] Marquez H J. Nonlinear Control Systems: Analysis and Design[M]. Hoboken: JohnWiley&Sons, Inc,2003.
[116] Amato F, Cosentino C, Mattei M, et al. A Nonlinear UIO Scheme for the FDI on aSmall Commercial Aircraft[C].2002IEEE International Conference on ControlApplications, Scotland, UK,2002:18-20.
[117] Patton R J, Chen J. Observer-based fault detection and isolation: robustness andapplications[J]. Control Engineering Practice,1997,5(5):671-682.
[118] Jiang B, Staroswiechi M. Adaptive Observer Design for Robust Fault Estimation[J].International journal of systems science,2002,33(9):767-775.
[119] Gao Z F, Jiang B, Zhang Y M, et al. Active fault-tolerant control design for T-Sfuzzy systems with application to a near space vehicle[C].2010IEEE InternationalSymposium on Intelligent Control, Yokohama, Japan,2010:123-128.
[120] Chen W, Jiang J. Fault-tolerant control against stuck actuator faults[J]. IEEProceedings: Control Theory and Applications,2005,152(2):138-146.
[121] Blanke M, Kinnaert M, Lunze J, et al. Diagnosis and Fault-Tolerant Control[M].Berlin: Springer-Verlag,2003.
[122] Ding S X. Model-Based Fault Diagnosis Techniques: Design Shemes Algorichmsand Tools[M]. Berlin: Springer-Verlag,2008.
[123] Patton R J, Frank P M, Clark R N. Issues of Fault Diagnosis for DynamicSystems[M]. Berlin: Springer-Verlag,2000.
[124] Qin S J, Li W. Detection and identification of faulty sensors in dynamicprocesses[J]. ALChE Journal,2001,47:1581-1593.
[125] Li W, Han Z G, Shah S L. Subspace identification for FDI in systems withnonuniformly sampled multirate data[J]. Automatica,2006,42:619-627.
[126] Qin S J. Data-driven fault detection and diagnosis for complex industrialprocesses[C]. In The7th IFAC symposium on fault detection, supervision andsafety of technical processes, Spain,2009:1115-1125.
[127] Naik A, Ding S X. Subspace based identification of diagnostic observer for Wienersystems[C]. In The7th IFAC symposium on fault detection, supervision and safetyof technical processes, Spain,2009:318-323.
[128] Wang Y L, Ma G F, Ding S X, et al. Subspace aided data-driven design of robustfault detection and isolation systems[J]. IEEE Transactions on Automatic Control,2011,47(11):2474-2480.
[129] Dong J F, Verhaenen M. Subspace based fault detection and identification for ltisystem[C]. In The7th IFAC symposium on fault detection, supervision and safetyof technical processes, Spain,2009:330-335.
[130] Kulcsar B, Dong J F, Verhaegen M. Model-free fault tolerant control approach forlinear parameter varying system[C]. In The7th IFAC symposium on fault detection,supervision and safety of technical processes, Spain,2009:876-881.
[131] Zhang X D, Parisini T, Polycarpou M M. Sensor bias fault isolation in a class ofnonlinear systems[J]. IEEE Transactions on Automatic Control,2005,50(3):370-376.
[132] Zhang X D, Polycarpou M M, Parisini T. Fault diagnosis of a class of nonlinearuncertain systems with lipschitz nonlinearities using adaptive estimation[J].Automatica,2010,46(2):290-299.
[133] Zhang X D. Sensor Bias Fault Detection and Isolation in a Class of NonlinearUncertain Systems Using Adaptive Estimation[J]. IEEE Transactions on AutomaticControl,2011,56(5):1220-1226.
[134] Zhang Y W, Joe S Q. Adaptive actuator/component fault compensation fornonlinear systems[J]. AIChE Journal,2008,54(9):2404-2412.
[135] Errari R, Parisini T, Polycarpou M M. Distributed fault diagnosis with overlappingdecompositions: An adaptive approximation approach[J]. IEEE Transactions onAutomatic Control,2009,54(4):794-799.
[136] Zhao J, Sun W, Song Y B, et al. Fault-tolerant PID controllers design for unknownnonlinear systems based on support vector machine[C].2010Chinese Control andDecision Conference,2010:711-716.
[137] Rodil S S, Fuente M J. Fault tolerance in the framework of support vector machinesbased model predictive control[J]. Engineering Applications of ArtificialIntelligence,2010,23(7):1127-1139.
[138] Zhang X D, Parisini T, Polycarpou M M. Adaptive fault-tolerant control ofnonlinear uncertain systems: an information-based diagnostic approach[J]. IEEETransactions on Automatic Control,2004:1259-1274.
[139]何熠.基于支持向量机的非线性系统辨识建模与控制[D].天津:天津大学博士学位论文,2006:49-64.
[140]薛文涛.基于免疫的智能优化算法理论及应用研究[D].南京:南京理工大学博士学位论文,2008:2-16.
[141]田蔚风.卫星姿态测量系统信息处理技术研究[D].上海:上海交通大学博士学位论文,1998:30-48.
[142]姜云春.基于模型的控制系统鲁棒故障诊断技术研究[D].长沙:国防科学技术大学博士学位论文,2006:77-98.
[143] Chen W. Robust fault diagnosis and compensation in nonlinear systems via slidingmode and iterative learning observers[D]. Canada: Simon Fraser University,2003:134-161.
[144] Chen W, Saif M. Observer-based fault diagnosis of satellite systems subject totime-varying thruster faults[J]. Journal of Dynamic Systems, Measurement andControl,2007,129(7):352-356.
[2] Gelderloss H, Young D. Redundancy management of shuttle flight control rategyroscopes and accelerometers[C]. Proceedings of the1982American ControlConference, Arlington, VA, USA,1982:808-811.
[3] Eryurek E, Upadhyaya B R. Fault tolerant control and diagnostic for large scalesystems[J]. IEEE Control Systems Magazine,1995,15(5):34-42.
[4] Garcia H E, Ray A, Edwards R M. Reconfigurable control of power plants usingautomata[J]. IEEE Control Systems Magazine,1991,11(11):85-92.
[5] Celi R, Huang C Y, Shih I C. Reconfigurable flight control for a tandem rotorhelicopter[C].52nd Annual National Forum of the American Helicopter Society,Washington, DC, USA,1996:1569-1588.
[6] Huang C Y, Stengel R F. Restructurable control using proportional-integral implicitmodel following[J]. Journal of Guidance, Control and Dynamics,1990,13(2):303-309.
[7] Mayback P S, Stevens R D. Reconfigurable flight control via multiple modeladaptive control methods[J]. IEEE Transactions on Aerospace and ElectronicSystems,1991,27(2):470-479.
[8] Morse W D, Ossman K A. Model following reconfigurable flight control system forthe AFTI/F-16[J]. Journal of Guidance, Control and Dynamics,1990,13(6):969-976.
[9] Samedov R. Approach to the support of the fault tolerance of the double redundantcomputer control systems[J]. Mathematical and Computational Applications,1999:175-180.
[10] Kobayashi Y, Ikeda M, Fujisaki Y. Fault-tolerant decentralized control for largespace structures[C]. Proceedings of the38th IEEE Conference on Decision andControl, Phoenix, AZ, USA,1999:303-308.
[11] Yeung W H R, Moore P R. Genetic algorithms and flexible process planning in thedesign of fault tolerant cell control for flexible assembly systems[J]. InternationalJournal of Computer Integrated Manufacturing,2000,13(2):157-161.
[12] Rauch H E. Autonomous control reconfiguration[J]. IEEE Control SystemsMagazine,1995,15(6):37-48.
[13] Burkholder J O. Adaptive Algorithms for Dynamic Systems Diagnostics and Fault-Tolerant Control[D]. Virginia: The University of Virginia,2010:1-3.
[14] Mack S, Tao G., Burkholder J O. An adaptive detection scheme for aircraftaerodynamic system damages[C].2010American Control Conference, VA, USA,2010:542-547.
[15] Henry D. Fault diagnosis of Microscope satellite thrusters using h∞/h filters[J].AIAA Journal of Guidance Control and Dynamics,2008,31(3):699–711.
[16] Edwards C, Lombaerts T, Smaili H. Fault Tolerant Flight Control A BenchmarkChallenge[M]. Berlin: Springer-Verlag,2010:131-140.
[17] Patton R J, Uppal F J, Simani S, et al. Robust FDI Applied to Thruster Faults of ASatellite System[J]. Control Engineering Practice,2010,18(9):1093-1109.
[18] Patton R J, Uppal F J, Simani S, et al. Reliable fault diagnosis scheme for aspacecraft attitude control system[J]. Journal of Risk and Reliability,2008,222(2):139-152.
[19] Beard R V. Failure Accommodation in Linear Systems through Self-reorganization[R]. Report MVT-71-1, MIT, Cambridge, Massachusetts,1971.
[20] Mehra R K, Peschon J. An Innovation Approach to Fault Detection and Diagnosisin Dynamics[J]. Automatica,1971,7:637-640.
[21] Willsky A S. A Survey of Design Methods for Failure Detection and Diagnosis inDynamics[J]. Automatica,1976,12:601-611.
[22] Himmelblau D M. Fault Detection and Diagnosis in Chemical and PetrochemicalProcess[M]. Amsterdam: Elsevier Press,1978,1-46.
[23]叶银忠,潘日芳,蒋慰孙.动态系统的故障检测与诊断方法[J].信息与控制,1985,15(6):27-34.
[24]周东华,孙优贤.控制系统的故障检测与诊断技术[M].北京:清华大学出版社,1994,1-24.
[25] Brown D W. A Prognostic Health Management Based Framework for Fault-Tolerant Control[D]. Atlanta: Georgia Institute of Technology,2011:33-40.
[26] Vachtsevanos G, Lewis F, Roemer M, et al. Intelligent fault diagnosis and prognosisfor engineering systems[M]. Hoboken: John Wiley&Sons,2007,172-279.
[27] Ding S X. Model-based Fault Diagnosis Techniques: Design Schemes, Algorithmsand Tools[M]. Berlin: Springer-Verlag,2008:1-10.
[28] Mahmoud M M, Jiang J, Zhang Y M. Active Fault Tolerant Control Systems:Stochastic Analysis and Synthesis[M]. Berlin: Springer-Verlag,2003:28-40.
[29] Jiang J, Yu X. Fault-tolerant control systems: A comparative study between activeand passive approaches[J]. Annual Reviews in Control,2012,36(1):60-70.
[30] Zhang Y M, Jiang J. Issues on integration of fault diagnosis and reconfigurablecontrol in active fault-tolerant control systems[C]. Proceedings of the6th IFACSymposium of Fault Detection Supervisionand Safety for Technical Processes,Beijing, China,2006:1513-1524.
[31] Benosman M. A survey of some recent results on nonlinear fault tolerant control[J].Mathematical Problems in Engineering, Hindawi Publishing Corporation,2009,vol.2010, paper586169.
[32] Saljak D D. Reliable Control Using Multiple Control Systems[J]. InternationalJournal of Control.1980,31:303-329.
[33] Vidyasagar M, Viswanadham N. Reliable Stabilization Using a multi-controllerconfiguration[J]. Automatica,1985,21(4):599-602.
[34] Gundes A N. Controller design for reliable stabilization[C]. Proceedings of12thIFAC World Congress,1993,4:1-4.
[35] Sebe N, Kitamori T. Reliable Stabilization Based on a Multi-CompensatorConfiguration[C]. Proceedings of12th IFAC World Congress,1993,4:5-8.
[36] Saeks R, Murray J. Fractional representation, algebraic geometry, and thesimultaneous stabilization problem[J]. IEEE Trans On Automatic Control,1982,24(4):895-903.
[37] Kabamba P T, Yang C. Simultaneous controller design for linear time-invariantsystems[J]. IEEE Trans On Automatic Control,1991,36(1):106-111.
[38] Olbrot A W. Fault tolerant control in the presence of noise: a new algorithm andsome open problems[C]. Proc. of12th IFAC World Congress,1993,7:467-470.
[39] Ackermann J. Robustness against Sensor Failures[J]. Automatica,1984,20(2):211-215.
[40] Morari M. Robust Stability of Systems with Integral Control[J]. IEEE Transactionson Automatic Control,1985,30:574-588.
[41] Huo Z H, Zheng Y, Xu C. A robust fault-tolerant control strategy for networkedcontrol systems[J]. Journal of Network and Computer Application,2011,34(2):708-714.
[42] Majid F, Amin N. Norm-bounded integrity conditions of uncertain multivariablelinear time-invariant systems under decentralized control with integral action[J].Journal of Process Control,2012,22(2):463-469.
[43]陈雪芹,耿云海,张迎春.基于LMI的鲁棒容错控制及其在卫星姿态控制中的应用[J].控制理论与应用,2008,25(1):95-99.
[44] Liang J J, Dong C Y, Wang Q. A Fault-tolerant Attitude Control System for aSatellite Based on Fuzzy Global Sliding Mode Control Algorithm[C].7thInternational Symposium on Instrumentation and Control Technology, Beijing,China,2008:127-128.
[45] Godard G, Kumar K D. Fault Tolerant Reconfigurable Satellite Formations UsingAdaptive Variable Structure Techniques[J]. Journal of Guidance, Control andDynamics,2010,33(3):969-984.
[46] Tudoroiu N, Khorasani K. Fault Detection and Diagnosis for Satellite's AttitudeControl System(ACS) Using an Interactive Multiple Model(IMM) Approach[C].IEEE Conference on Control Applications, Toronto, Canada, August28~31,2005:1287-1292.
[47]陈雪芹,张迎春,耿云海,等.基于IMM/EA的卫星姿态控制系统重构容错控制[J].系统工程与电子技术,2007,29(5):774-777.
[48] Fan L L, Song Y D. Model reference adaptive fault-tolerant control with applicationto wind turbines[C].2011Chinese Control and Decision Conference, Beijing,China,2011:1314-1319.
[49] Wang J, Rugh W J. Feedback Linearization Families for Nonlinear Systems[J].IEEE Transactions on Automatic Control,1987,32(10):935-940.
[50] Calise A J, Hovakimyan N, Idan M. Adaptive output feedback control of nonlinearsystems using neural networks[J]. Automatica,2001,37(8):1201-1211.
[51] Benjamin J O, Liu J F, David M P, et al. Data-based fault detection and isolationusing feedback control: Output feedback and optimality[J]. Chemical EngineeringScience,2009,64(10):2370-2383.
[52] Halim A, Chrisropher E, Andres M. Fault reconstruction using a LVP sliding modeobserver for a class of LVP systems[J]. Journal of the Franklin Institute,2012,349(2):510-530.
[53] Chrisropher E, Chee P T. Sensor fault tolerant control using sliding modeobservers[J]. Control Engineering Practice,2006,14(8):897-908.
[54] Varma S, Kumar K D. Fault tolerant satellite attitude control using solar radiationpressure based on nonlinear adaptive sliding mode[J]. Acta Astronautica,2010,66(3):486-500.
[55] Halim A, Chrisropher E. Fault tolerant control using sliding modes with on-linecontrol allocation[J]. Automatic,2008,44(7):1859-1866.
[56] Liao F, Lum K Y, Wang J L, et al. Adaptive control allocation for non-linearsystems with internal dynamics[J]. IET Control Theory and Application,2010,4(6):909-922.
[57] Andry A N, Shapiro E Y, Chung J C. Eigenstructure assignment for linearsystems[J]. IEEE Transactions on Aerospace Electronic Systems,1983,19(5):711-729.
[58] Cai G B, Hu C H, Duan G R. Eigenstructure assignment for linear parameter-varying systems with applications[J]. Mathematical and Computer Modelling,2011,53(5):861-870.
[59] Maciejowski J M. Predictive control with constraints[M]. Prentice Hall, EnglewoodCliffs,2002.
[60] Rodil S S, Fuente M J. Fault tolerance in the framework of support vector machinesbased model predictive control[J]. Engineering Applications of ArtificialIntelligence,2010,23(7):1127-1139.
[61] Anjali P D, Sachin C P, Shankar S N. Intelligent state estimation for fault tolerantnonlinear predictive control[J]. Journal of Process Control,2009,19(2):187-204.
[62] Verhaegen M, Verdult V. Filtering and systerm identification: an introduction[M].Cambridge University Press, Cambridge,2007.
[63] Wu Q, Saif M. Robust Fault Diagnosis for A Satellite Large Angle Attitude SystemUsing An Iterative Neuron PID(INPID) Observer[C]. In2006American ControlConference, New York,2006:5710-5715.
[64] Chen W, Saif M. Observer-based Fault Diagnosis of Satellite Systems Subject toTime-varying Thruster Faults[J]. Journal of Dynamic Systems Measurement andControl,2007,129(3):352-356.
[65] Hou Q, Cheng Y H, Lu N Y, et al. Study on FDD and FTC of Satellite AttitudeControl System Based on the Effectiveness Factor[C]. In2nd InternationalSymposium on Systems and Control in Aerospace and Astronautics, Shenzhen,China,2008:1096-1101.
[66] Jiang T, Khorasani K. A Fault Detection, Isolation and Reconstruction Strategy forA Satellite's Attitude Control Subsystem with Redundant Reaction Wheels[C]. InIEEE International Conference on Systems, Man and Cybernetics, Montreal, CookIslands,2007:1644-1650.
[67] Henry D. Fault diagnosis of Microscope Satellite Thrusters Using H-infinity/H-Filters[J]. Journal of Guidance, Control and Dynamics,2008,31(3):699-711.
[68] Talebi H A, Patel R V. An Intelligent Fault Detection and Recovery Scheme forReaction Wheel Actuator of Satellite Attitude Control Systems[C]. Proceedings ofthe2006IEEE International Conference on Control Applications,2006:2099-2104.
[69] Li Z Q, Ma L, Khorasani K. A Dynamic Neural Network-based Reaction WheelFault Diagnosis for Satellites[C]. In International Joint Conference on NeuralNetworks Vancouver, Canada,2006:3714-3721.
[70] Ji C W, Rong J J. Development of Fault Diagnosis System for Spacecraft Based onFault Tree and G2[J]. Journal of Beijing Institute of Technology,2002,11(4):444-448.
[71]侯忠生,许建新.数据驱动控制理论及方法的回顾和展望[J].自动化学报,2009,35(6):650-667.
[72] Uchiyama M. Formulation of high-speed motion pattern of a mechanical arm bytrial[J]. Transactions of the Society for Instrumentation and Control Engineering,1978,14(6):706-712.
[73] Arimoto S, Kawamura S, Miyazaki F. Bettering operation of robots by learning[J].Journal of Robotic Systems,1984,1(2):123-140.
[74] Ahn H S, Chen Y Q, Moore K L. Iterative learning control: brief survey andcategorization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews,2007,37(6):1099-1121.
[75] Hou Z S, Xu J X, Yan J W. An iterative learning approach for density control offreeway traffic flow via ramp metering[J]. Transportation Research, Part C:Emerging Technologies,2008,16(1):71-97.
[76] Ding S X, Zhang P, Naik A, et al. Subspace method aided data-driven design offault detection and isolation systems[J]. Journal of Process Control,2009,19(9):1496-1510.
[77] Lopes J A, Menezes J C. Multivariate monitoring of fermentation processes withnon-linear modelling methods[J]. Analytica Chimica Acta,2004,515(1):101-108.
[78] Patan K, Witczak M, Korbicz J. Towards robustness in neural network based faultdiagnosis[J]. International Journal of Applied Mathematics and Computer Sciences,2008,18(4):443-454.
[79] Fuessel D, Isermann R. Hierarchical motor diagnosis utilizing structural knowledgeand a self-learning neuro-fuzzy scheme[J]. IEEE Transactions on IndustrialElectronics,2000,47(5):1070-1077.
[80] Koppen-Seliger B, Frank P M. Fuzzy logic and neural networks in faultdetection[M]. Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms,New York: CRC Press,1999:169-209.
[81] Oza N C, Tumer I Y, Tumer K, et al. Classification of Aircraft Maneuvers for FaultDetection[C]. In Proceedings of Multiple Classifier Systems,2003:375–384.
[82] Park H, Mackey R, James M, et al. Analysis of Space Shuttle Main Engine DataUsing Beacon-based Exception Analysis for Multi-Missions[C]. In Proceedings ofthe IEEE Aerospace Conference, New York,2002,6:2835-2844.
[83] He F J, Shi W G. WPT-SVMs Based Approach for Fault Detection of Valves inReciprocating Pumps[C]. Proceedings of the American Control Conference,Anchorage, AK,2002:4566-4570.
[84] Schwabacher M. Machine Learning for Rocket Propulsion Health Monitoring[C].Proceedings of the SAE World Aerospace Congress, Dallas, TX,2005.
[85] Iverson D L. Inductive System Health Monitoring[C]. Proceedings of theInternational Conference on Artificial Intelligence, Las Vegas, USA,2004:1-7.
[86] Wegerich S W, Wilks A D, Pipke R M. Nonparametric modeling of vibration signalfeatures for equipment health monitoring[C]. Proceedings of IEEE AerospaceConference, New York,2003,7:3113–3121.
[87] Atlas L, Bloor G, Brotherton T, et al. An evolvable tri-reasoner IVHM system[C].Proceedings of the IEEE Aerospace Conference, New York,2001,6:3023-3037.
[88]章仁为.卫星轨道姿态动力学与控制[M].北京:北京航空航天大学出版社,1998:139-140.
[89] Barua A, Khorasani K. Hierarchical Fault Diagnosis and Health Monitoring inMulti-platform Space Systems[C]. IEEE of Aerospace conference,2009:1-13.
[90]于翠.飞轮控制系统的设计与实现[D].哈尔滨:哈尔滨工业大学硕士学位论文,2006:19-21.
[91]程颢.飞轮控制系统研究与设计[D].哈尔滨:哈尔滨工业大学博士学位论文,2009:96-109.
[92] Noura H, Theilliol D, Ponsart J C. Fault-tolerant Control Systems Design andPractical Applications[M]. Berlin: Springer-Verlag,2009.
[93]王小丽,倪茂林.基于自适应观测器的非线性系统故障诊断[J].空间控制技术与应用,2008,34(4):33-37.
[94]杨保华.航天器制导、导航与控制[M].北京:中国科学技术出版社,2011,276-286.
[95]田科丰,李智斌.基于自适应观测器的飞轮故障诊断方法[J].华中科技大学学报,2009,37(Sup. I):184-186.
[96]陈雪芹.集成故障诊断与容错控制研究及其在卫星姿态控制中的应用[D].哈尔滨:哈尔滨工业大学博士学位论文,2008:27-29.
[97]纪延琚.卫星姿态控制系统层次化故障诊断系统设计与实现[D].哈尔滨:哈尔滨工业大学硕士学位论文,2011:23-25.
[98]刘一武,陈义庆.星敏感器测量模型及其在卫星姿态确定系统中的应用[J].宇航学报,2003,24(2):162-167.
[99] Das R K, Sen S, Dasgupta S. Fault tolerant controller for attitude control of satellitelaunch vehicle via LMI approach[C]. Proceeding of the IEEE INDICON2004FirstIndia Annual Conference,2004:304-309.
[100] Ye S J, Zhang Y M, Wang X M, et al. An improved LMI approach for Static OutputFeedback Fault-Tolerant Control with application to flight tracking control[C].4thIEEE Conference on Industrial Electronics and Applications, Xi’an, China,2009:35-40.
[101] Ye S J, Zhang Y M, Rabbath C A, et al. An LMI approach to mixed H1/H∞robustfault-tolerant control design with uncertainties[C]. American Control Conference2009, St. Louis, MO,2009:5540-5545.
[102]张嗣瀛,高立群.现代控制理论[M].北京:清华大学出版社,2006:246-247.
[103]王德进. H2和H∞优化控制理论[M].哈尔滨:哈尔滨工业大学出版社,2001:72-111.
[104] Xie L H, De S, Carlos E. Robust H∞control for linear systems with norm-boundedtime-varying uncertainty[C]. IEEE Transactions on Automatic Control,1992,37(8pt I):1188-1191.
[105]陈雪芹,张迎春,耿云海,等.基于控制有效因子的卫星姿态控制系统在轨重构容错控制[J].宇航学报,2007,28(1):94-98.
[106]侯倩,程月华,姜斌,等.基于RO-NUIO/LMI的挠性卫星轨控期间姿控系统故障诊断[J].宇航学报,2011,32(10):2146-2151.
[107] Gao Z F, Jiang B, Shi P, et al. Sensor fault estimation and compensation formicrosatellite attitude control systems[J]. International Journal of ControlAutomation and Systems,2010,8(2):228-237.
[108]陈雪芹,耿云海,张世杰,等.基于混合H2/H∞的集成故障诊断与容错控制研究[J].宇航学报,2007,28(4):890-896.
[109] Hu Q L. Robust adaptive sliding-mode fault-tolerant control with L(2)-gainperformance for flexible spacecraft using redundant reaction wheels[J]. IETControl Theory and Applications,2010,4(6):1055-1070.
[110]张冬梅,孙明轩,俞立.基于观测器跟踪非一致轨迹的迭代学习控制器设计[J].控制理论与应用,2006,23(5):795-799.
[111] Chen W, Saif M. An iterative learning observer-based approach to fault detectionand accommodation in nonlinear systems[C]. Proceedings of the40th IEEEConference on Decision and Control, Orlando, USA,2001:4469-4474.
[112] Mondal S, Chakraborty G, Bhattacharyya K. LMI approach to robust unknowninput observer design for continuous systems with noise and uncertainties[J].International Journal of Control, Automation and Systems,2010,8(2):210-219.
[113] Wang D, Lum K Y. Adaptive unknown input observer approach for aircraft actuatorfault detection and isolation[J]. International Journal of Adaptive Control andSignal Processing,2007,21(1):31-48.
[114]肖冰,胡庆雷,马广富.基于观测器的航天器执行机构失效故障重构[J].宇航学报,2011,32(2):323-328.
[115] Marquez H J. Nonlinear Control Systems: Analysis and Design[M]. Hoboken: JohnWiley&Sons, Inc,2003.
[116] Amato F, Cosentino C, Mattei M, et al. A Nonlinear UIO Scheme for the FDI on aSmall Commercial Aircraft[C].2002IEEE International Conference on ControlApplications, Scotland, UK,2002:18-20.
[117] Patton R J, Chen J. Observer-based fault detection and isolation: robustness andapplications[J]. Control Engineering Practice,1997,5(5):671-682.
[118] Jiang B, Staroswiechi M. Adaptive Observer Design for Robust Fault Estimation[J].International journal of systems science,2002,33(9):767-775.
[119] Gao Z F, Jiang B, Zhang Y M, et al. Active fault-tolerant control design for T-Sfuzzy systems with application to a near space vehicle[C].2010IEEE InternationalSymposium on Intelligent Control, Yokohama, Japan,2010:123-128.
[120] Chen W, Jiang J. Fault-tolerant control against stuck actuator faults[J]. IEEProceedings: Control Theory and Applications,2005,152(2):138-146.
[121] Blanke M, Kinnaert M, Lunze J, et al. Diagnosis and Fault-Tolerant Control[M].Berlin: Springer-Verlag,2003.
[122] Ding S X. Model-Based Fault Diagnosis Techniques: Design Shemes Algorichmsand Tools[M]. Berlin: Springer-Verlag,2008.
[123] Patton R J, Frank P M, Clark R N. Issues of Fault Diagnosis for DynamicSystems[M]. Berlin: Springer-Verlag,2000.
[124] Qin S J, Li W. Detection and identification of faulty sensors in dynamicprocesses[J]. ALChE Journal,2001,47:1581-1593.
[125] Li W, Han Z G, Shah S L. Subspace identification for FDI in systems withnonuniformly sampled multirate data[J]. Automatica,2006,42:619-627.
[126] Qin S J. Data-driven fault detection and diagnosis for complex industrialprocesses[C]. In The7th IFAC symposium on fault detection, supervision andsafety of technical processes, Spain,2009:1115-1125.
[127] Naik A, Ding S X. Subspace based identification of diagnostic observer for Wienersystems[C]. In The7th IFAC symposium on fault detection, supervision and safetyof technical processes, Spain,2009:318-323.
[128] Wang Y L, Ma G F, Ding S X, et al. Subspace aided data-driven design of robustfault detection and isolation systems[J]. IEEE Transactions on Automatic Control,2011,47(11):2474-2480.
[129] Dong J F, Verhaenen M. Subspace based fault detection and identification for ltisystem[C]. In The7th IFAC symposium on fault detection, supervision and safetyof technical processes, Spain,2009:330-335.
[130] Kulcsar B, Dong J F, Verhaegen M. Model-free fault tolerant control approach forlinear parameter varying system[C]. In The7th IFAC symposium on fault detection,supervision and safety of technical processes, Spain,2009:876-881.
[131] Zhang X D, Parisini T, Polycarpou M M. Sensor bias fault isolation in a class ofnonlinear systems[J]. IEEE Transactions on Automatic Control,2005,50(3):370-376.
[132] Zhang X D, Polycarpou M M, Parisini T. Fault diagnosis of a class of nonlinearuncertain systems with lipschitz nonlinearities using adaptive estimation[J].Automatica,2010,46(2):290-299.
[133] Zhang X D. Sensor Bias Fault Detection and Isolation in a Class of NonlinearUncertain Systems Using Adaptive Estimation[J]. IEEE Transactions on AutomaticControl,2011,56(5):1220-1226.
[134] Zhang Y W, Joe S Q. Adaptive actuator/component fault compensation fornonlinear systems[J]. AIChE Journal,2008,54(9):2404-2412.
[135] Errari R, Parisini T, Polycarpou M M. Distributed fault diagnosis with overlappingdecompositions: An adaptive approximation approach[J]. IEEE Transactions onAutomatic Control,2009,54(4):794-799.
[136] Zhao J, Sun W, Song Y B, et al. Fault-tolerant PID controllers design for unknownnonlinear systems based on support vector machine[C].2010Chinese Control andDecision Conference,2010:711-716.
[137] Rodil S S, Fuente M J. Fault tolerance in the framework of support vector machinesbased model predictive control[J]. Engineering Applications of ArtificialIntelligence,2010,23(7):1127-1139.
[138] Zhang X D, Parisini T, Polycarpou M M. Adaptive fault-tolerant control ofnonlinear uncertain systems: an information-based diagnostic approach[J]. IEEETransactions on Automatic Control,2004:1259-1274.
[139]何熠.基于支持向量机的非线性系统辨识建模与控制[D].天津:天津大学博士学位论文,2006:49-64.
[140]薛文涛.基于免疫的智能优化算法理论及应用研究[D].南京:南京理工大学博士学位论文,2008:2-16.
[141]田蔚风.卫星姿态测量系统信息处理技术研究[D].上海:上海交通大学博士学位论文,1998:30-48.
[142]姜云春.基于模型的控制系统鲁棒故障诊断技术研究[D].长沙:国防科学技术大学博士学位论文,2006:77-98.
[143] Chen W. Robust fault diagnosis and compensation in nonlinear systems via slidingmode and iterative learning observers[D]. Canada: Simon Fraser University,2003:134-161.
[144] Chen W, Saif M. Observer-based fault diagnosis of satellite systems subject totime-varying thruster faults[J]. Journal of Dynamic Systems, Measurement andControl,2007,129(7):352-356.