卫星姿态控制系统中的故障诊断研究
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摘要
卫星姿态控制系统是保障卫星正常运转的核心子系统。由于卫星在太空运行过程中具有远程不可及等应用特殊性,其姿态控制系统的故障诊断问题一直备受航天工业界学者及研究人员关注。卫星星上资源和人工干预能力有限,太空监测环境恶劣和不确定性因素多等特点决定了卫星故障诊断不仅要具备一般故障诊断的可靠性及准确性要求,还必须具有快速自诊断及自主容错恢复能力。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,本文在863高技术计划项目(2007AA04Z438)资助下,以卫星姿态控制系统中的实际故障问题为切入点,从模型(定量)途径、智能(定性)途径及模型与智能混合途径三方面系统研究了故障检测、分离、估计与恢复等故障诊断问题及技术。
基于对象系统模型研究了卫星姿控系统中的故障诊断与容错问题。针对执行器非完全失效这一典型故障,提出了以自适应观测器与扩张状态观测器相结合作为故障诊断与估计手段、采用四元数控制律故障调节技术作为恢复措施的闭环被动容错策略,可实现执行器非完全失效故障情形下的卫星姿控系统故障检测、分离与恢复。针对传感器故障失效这一问题,提出了一种以奉献观测器作为故障检测与隔离手段、采用KX观测器实现传感器故障容错观测的闭环主动容错方法,可克服因部分传感器观测失效导致的整体观测失效及闭环控制不稳定问题,使得卫星姿态控制系统仍能被部分观测并保障其闭环控制稳定性,实现传感器失效情形下的卫星姿态控制系统故障检测、分离与恢复。
基于计算智能研究了以神经网络模式分类为核心方法、面向卫星姿态控制系统实时信号的故障诊断新方法。针对红外地球仪故障,采用双层ELMAN动态神经网络,对故障时域信号进行样本学习及模式分类,实现故障检测与故障隔离。针对故障检测的高实时性要求与组合故障隔离问题,引入改进平移小波实时获取故障信号奇异点的模极大值,避免故障检测依赖样本学习的缺陷,在故障检测基础上采用改进动态循环神经网络(Improved Dynamic Recurrent Neural Network, IDRNN)进行智能故障识别,可以实现面向监测实时信号的故障检测、及复合模式分类。
基于模型与计算智能混合途径研究了以神经网络模型辨识为核心方法、面向飞轮进行离线辨识与在线观测结合的故障诊断新方法。针对飞轮正常及故障模式,通过离线设计与训练多个BPNN神经网络辨识模型并作为估计器在线监测生成残差,可实现基于残差的飞轮故障检测及故障程度区分。针对优化模型精度实现准确故障评估的问题,提出了一种灰盒神经网络模型辨识与故障评估方案,通过直接继承对象系统动力学并采用改进的自定义激励训练策略,获得正常模式下的灰盒神经网络辨识模型并作为估计器在线嵌入生成残差,可避免非线性动态系统模型辨识中由于动力学不匹配所导致的模型精度下降缺陷,不依赖故障模型即可实现故障检测及故障评估。
研究了验证故障诊断方法及结论的相关基础软硬件平台构建问题。构建了一套基于xPC Target的卫星姿态控制系统实时数学仿真软硬件平台,以某型三轴对地定向卫星为蓝本,建立了用于故障注入及故障模拟的完整卫星姿控系统模型,基于该平台可实现故障征兆分析及实现对上述诊断方法的验证。
Satellite Attitude Control System (SACS) is the key subsystem of an artificial satellite. As the satellites are unreachable in remote space, Fault Diagnosis (FD) on SACS is a hot and frontable problem in the FD research area. More importantly, satellite also has other disadvantages, such as the limited onboard resource in satellites, limited operation ability by human being, bad space environment, and more uncertainty. Those entire disadvantages make a requirement that FD of Satellite must has the ability of quickly self-diagnosis and the ability of being tolerant with fault itself. With the requirement on reliability and long-life of key plant or equipment of china and sponsored by china 863 high technique program (2007AA04Z438), aimed on the practical problem of SACS, this thesis do researches on fault detection, isolation, estimation and recovery from three main approach:model-based (quantitive), intelligent-based (qualitative), hybrid-based.
Following the model-based approach, fault diagnosis and fault-tolerant problems of SACS are researched. Aimed on the partial loss of effect (LOE) fault of actuators, a combination with adaptive observer and extended state observer is proposed to diagnosis faults and estimate the severity of fault, and fault accommodation based on quaternion-feedback control low is proposed to recover the fault and implement passive fault tolerant in close-loop. Based on the work above, the partial loss of effect (LOE) fault of actuators in SACS can be detected, isolated and accommodated. Aimed on the partial loss of effect (LOE) fault of sensors, FDI based on devoting observer and active fault-tolerant measures with sensor fault in close-loop fault-tolerant based on KX observer is proposed to overcome full LOE and close-loop instability, which results from that partial sensor is wrong, and make SACS being observed in part and stable in close-loop. Based on the work above, Aimed on the partial loss of effect (LOE) fault of sensors in SACS can be detected, isolated and recovery.
Following the intelligent-based approach, we do research on FD of SACS based on the real-time signal by using some neural network (NN) techniques. Aimed on the fault of infrared earth sensor, double ELMAN dynamical NNs are used to learn from the transient signal and classify the specified fault modes in order to detect and isolate the faults. Aimed on the real-time requirement of FD and isolation on multiple faults, an improved MALLET wavelet is introduced to obtain the maximum modulus of signal singular point and avoid learning from samples in order to detect fault, and then an improved IDRNN (Improved Dynamic Recurrent Neural Network) is proposed to identify the faults. Based on the works above, we can detect faults and isolate multiple faults based on real-time monitoring signal.
Following the hybrid-based approach, a novel FD for Reaction Wheel (RW) by using NN model identification is proposed. Aimed on the fault mode and fault-free mode in RW, multiple BPNN identification model are designed and trained offline, and then all of them are embedded into object system in order to generate diagnosis residuals online. Based on the residuals, RW faults with different severities can be detected and differed. Aimed on the fault estimation (FE) problem depending on model accuracy, grey box neural network model identification and fault estimation is proposed. By inheriting the dynamics of the object system directly and introducing an improved self-defined exciting strategy, the grey box neural network model for normal mode can be obtained to generate diagnosis residuals online as an estimator so that unmatched dynamics can be avoided. Based on the GBNNM model, faults can be detected and estimated but fault models are not essential.
Finally, how to develop the software and hardware in order to validate the proposed FD mentioned above is studied. A real-time simulation hardware platform based on xPC Target, which is used to simulate the SACS, is constructed. And then with referring to some type three-axis earth-oriented satellite, a complete SACS model is designed to inject faults and simulate the fault's behavior. Based on the software/hardware platform, faults mechanism can be analyzed and FD can be validated.
基于对象系统模型研究了卫星姿控系统中的故障诊断与容错问题。针对执行器非完全失效这一典型故障,提出了以自适应观测器与扩张状态观测器相结合作为故障诊断与估计手段、采用四元数控制律故障调节技术作为恢复措施的闭环被动容错策略,可实现执行器非完全失效故障情形下的卫星姿控系统故障检测、分离与恢复。针对传感器故障失效这一问题,提出了一种以奉献观测器作为故障检测与隔离手段、采用KX观测器实现传感器故障容错观测的闭环主动容错方法,可克服因部分传感器观测失效导致的整体观测失效及闭环控制不稳定问题,使得卫星姿态控制系统仍能被部分观测并保障其闭环控制稳定性,实现传感器失效情形下的卫星姿态控制系统故障检测、分离与恢复。
基于计算智能研究了以神经网络模式分类为核心方法、面向卫星姿态控制系统实时信号的故障诊断新方法。针对红外地球仪故障,采用双层ELMAN动态神经网络,对故障时域信号进行样本学习及模式分类,实现故障检测与故障隔离。针对故障检测的高实时性要求与组合故障隔离问题,引入改进平移小波实时获取故障信号奇异点的模极大值,避免故障检测依赖样本学习的缺陷,在故障检测基础上采用改进动态循环神经网络(Improved Dynamic Recurrent Neural Network, IDRNN)进行智能故障识别,可以实现面向监测实时信号的故障检测、及复合模式分类。
基于模型与计算智能混合途径研究了以神经网络模型辨识为核心方法、面向飞轮进行离线辨识与在线观测结合的故障诊断新方法。针对飞轮正常及故障模式,通过离线设计与训练多个BPNN神经网络辨识模型并作为估计器在线监测生成残差,可实现基于残差的飞轮故障检测及故障程度区分。针对优化模型精度实现准确故障评估的问题,提出了一种灰盒神经网络模型辨识与故障评估方案,通过直接继承对象系统动力学并采用改进的自定义激励训练策略,获得正常模式下的灰盒神经网络辨识模型并作为估计器在线嵌入生成残差,可避免非线性动态系统模型辨识中由于动力学不匹配所导致的模型精度下降缺陷,不依赖故障模型即可实现故障检测及故障评估。
研究了验证故障诊断方法及结论的相关基础软硬件平台构建问题。构建了一套基于xPC Target的卫星姿态控制系统实时数学仿真软硬件平台,以某型三轴对地定向卫星为蓝本,建立了用于故障注入及故障模拟的完整卫星姿控系统模型,基于该平台可实现故障征兆分析及实现对上述诊断方法的验证。
Satellite Attitude Control System (SACS) is the key subsystem of an artificial satellite. As the satellites are unreachable in remote space, Fault Diagnosis (FD) on SACS is a hot and frontable problem in the FD research area. More importantly, satellite also has other disadvantages, such as the limited onboard resource in satellites, limited operation ability by human being, bad space environment, and more uncertainty. Those entire disadvantages make a requirement that FD of Satellite must has the ability of quickly self-diagnosis and the ability of being tolerant with fault itself. With the requirement on reliability and long-life of key plant or equipment of china and sponsored by china 863 high technique program (2007AA04Z438), aimed on the practical problem of SACS, this thesis do researches on fault detection, isolation, estimation and recovery from three main approach:model-based (quantitive), intelligent-based (qualitative), hybrid-based.
Following the model-based approach, fault diagnosis and fault-tolerant problems of SACS are researched. Aimed on the partial loss of effect (LOE) fault of actuators, a combination with adaptive observer and extended state observer is proposed to diagnosis faults and estimate the severity of fault, and fault accommodation based on quaternion-feedback control low is proposed to recover the fault and implement passive fault tolerant in close-loop. Based on the work above, the partial loss of effect (LOE) fault of actuators in SACS can be detected, isolated and accommodated. Aimed on the partial loss of effect (LOE) fault of sensors, FDI based on devoting observer and active fault-tolerant measures with sensor fault in close-loop fault-tolerant based on KX observer is proposed to overcome full LOE and close-loop instability, which results from that partial sensor is wrong, and make SACS being observed in part and stable in close-loop. Based on the work above, Aimed on the partial loss of effect (LOE) fault of sensors in SACS can be detected, isolated and recovery.
Following the intelligent-based approach, we do research on FD of SACS based on the real-time signal by using some neural network (NN) techniques. Aimed on the fault of infrared earth sensor, double ELMAN dynamical NNs are used to learn from the transient signal and classify the specified fault modes in order to detect and isolate the faults. Aimed on the real-time requirement of FD and isolation on multiple faults, an improved MALLET wavelet is introduced to obtain the maximum modulus of signal singular point and avoid learning from samples in order to detect fault, and then an improved IDRNN (Improved Dynamic Recurrent Neural Network) is proposed to identify the faults. Based on the works above, we can detect faults and isolate multiple faults based on real-time monitoring signal.
Following the hybrid-based approach, a novel FD for Reaction Wheel (RW) by using NN model identification is proposed. Aimed on the fault mode and fault-free mode in RW, multiple BPNN identification model are designed and trained offline, and then all of them are embedded into object system in order to generate diagnosis residuals online. Based on the residuals, RW faults with different severities can be detected and differed. Aimed on the fault estimation (FE) problem depending on model accuracy, grey box neural network model identification and fault estimation is proposed. By inheriting the dynamics of the object system directly and introducing an improved self-defined exciting strategy, the grey box neural network model for normal mode can be obtained to generate diagnosis residuals online as an estimator so that unmatched dynamics can be avoided. Based on the GBNNM model, faults can be detected and estimated but fault models are not essential.
Finally, how to develop the software and hardware in order to validate the proposed FD mentioned above is studied. A real-time simulation hardware platform based on xPC Target, which is used to simulate the SACS, is constructed. And then with referring to some type three-axis earth-oriented satellite, a complete SACS model is designed to inject faults and simulate the fault's behavior. Based on the software/hardware platform, faults mechanism can be analyzed and FD can be validated.
引文
[1]林来兴.1990-2001年航天器制导、导航与控制系统故障分析研究.国际太空,2005(5)：9-13.
[2]邢琰,吴宏鑫,王晓磊等.航天器故障诊断与容错控制技术综述.宇航学报,2003,24(3)：221-226.
[3]张宗美.航天故障手册.宇航出版社,1994.
[4]姜连祥,李华旺,杨根庆等.航天器自主故障诊断技术研究进展.宇航学报,2009,30(4)：1320-1326.
[5]Talebi H A, Patel R V, Khorasani K. Fault detection and isolation for uncertain nonlinear systems with application to a satellite reaction wheel actuator.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3140-3145.
[6]Talebi H A, Khorasani K. A robust fault detection and isolation scheme with application to magnetorquer type actuators for satellites.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007: 3165-3170.
[7]Li L, Ma L, Khorasani K. A Dynamic Recurrent Neural Network Fault Diagnosis and Isolation Architecture for Satellite's Actuator/Thruster Failures. Second International Symposium on Neural Networks. (ISNN 2005), May 30-June 1, 2005.3:574-583.
[8]Al-Zyoud I, Khorasani K. Neural Network-based Actuator Fault Diagnosis for Attitude Control Subsystem of a Satellite.2006 World Automation Congres. (WAC'06), June 24-June 26,2006:1-6.
[9]Al-Zyoud A D, Khorasani K. Neural network-based actuator fault diagnosis for attitude control subsystem of an unmanned space vehicle. International Joint Conference on Neural Networks 2006. (IJCNN'06), July 16-July 21,2006: 3686-3693.
[10]Li Z Q, Ma L, Khorasani K. A dynamic neural network-based reaction wheel fault diagnosis for satellites:Neural Networks. International Joint Conference on Neural Networks 2006. (IJCNN'06), July 16-July 21,2006:3714-3721.
[11]Jiang T, Khorasani K. A fault detection, isolation and reconstruction strategy for a satellite's attitude control subsystem with redundant reaction wheels.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3146-3152.
[12]Saif M, Chen W, Wu Q. High Order Sliding Mode Observers and Differentiators-Application to Fault Diagnosis Problem. Modern Sliding Mode Control Theory,2008:321-344.
[13]Tudoroiu N, Khorasani K. Fault detection and diagnosis for satellite's attitude control system (ACS) using an interactive multiple model (IMM) approach.2005 IEEE International Conference on Control Applications. (CCA),28-31 Aug.2005: 1287-1292.
[14]陈雪芹,耿云海,张世杰等.基于混合H2H∞的集成故障诊断与容错控制研究.宇航学报,2007,28(4)：890-896.
[15]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.系统工程与电子技术,2007,29(5)：774-777.
[16]荣吉利.基于模型的航天器在轨传感器故障诊断方法.兵工学报,2002,23(2)：242-245.
[17]段晨阳,汤国建,张世杰.单轴飞轮故障时的小卫星姿态控制方法研究.航天控制,2007,25(3)：48-52.
[18]吴丽娜.卫星姿态控制系统故障诊断研究[硕士学位论文].哈尔滨工业大学,2006.
[19]Williams B C, Nayak P P. A model-based approach to reactive self-configuring systems. The Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference.4-8 Aug. 1996.2:971-978.
[20]Bernard D E, Gamble E B, Rouquette N F, et al. Remote Agent Experiment DS1 Technology Validation Report:Ames Research Center and JPL[Report].2000.
[21]Meyer C M, Fulton C, Maul B, et al. Propulsion IVHM Technology Experiment Overview. IEEE Aerospace Conference 2003. March 8,2003:859-868.
[22]Hayden S, Sweet A, Christa S E. Livingstone model-based diagnosis of Earth Observing One. Collection of Technical Papers-AIAA 1st Intelligent Systems Technical Conference, September 20-23,2004:98-108.
[23]Hayden S C, Sweet A J, Christa S E, et al. Advanced Diagnostic System on Earth Observing One. A Collection of Technical Papers-AIAA Space 2004 Conference and Exposition. September 28-30,2004:2209-2222.
[24]Hayden S C, Sweet A J, Shulman S. Lessons learned in the Livingstone 2 on Earth Observing One flight experiment. Info Tech at Aerospace:Advancing Contemporary Aerospace Technologies and Their Integration. September 26-29, 2005:863-877.
[25]M S, J S, L B. The NASA integrated vehicle health management techonology experiment for X-37. the SPIE AeroSense 2002 Symposium, Florida, April 3, 2002:1-12.
[26]刘洪刚,吴建军,陈启智.基于模型知识的液体火箭发动机故障诊断方法研究.宇航学报,2002,23(2)：41-43.
[27]郑威,吴建军.液体火箭发动机基于定性键合图模型的故障诊断方法研究.宇航学报,2004,25(6)：604-608.
[28]朱永娇,刘洪刚.基于定性模型和定量知识集成的智能故障诊断方法研究.科学技术与工程,2007,7(13)：3107-3110.
[29]彭蓉,秦永元.基于多敏感器卫星姿态确定系统故障检测方法研究.机械强度, 2007,29(3)：487-491.
[30]姚敏,赵敏.小卫星多级故障诊断系统设计.中国空间科学技术,2007,27(2)：47-51.
[31]杨家军,马兴瑞.用模糊聚类算法快速定位小卫星在轨运行故障.空间科学学报,1999,19(4)：354-361.
[32]Mohammadi R, Hashtrudi-Zad S, Khorasani K. A hybrid architecture for diagnosis in hybrid systems with applications to spacecraft propulsion system.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3184-3190.
[33]Tansel I N, Li M, Yapiei A, et al. Integrated Systems Health Monitoring for Autonomous Space Access Vehicles and Satellites. The 3rd International Conference on Recent Advances in Space Technologies. (RAST 2007), June 14-16, 2007:187-192.
[34]Yairi T, Kawahara Y, Fujimaki R, et al. Telemetry-mining:a machine learning approach to anomaly detection and fault diagnosis for space systems.2nd IEEE International Conference on Space Mission Challenges for Information Technology. (SMC-IT 2006), July 17-20,2006:466-473.
[35]Khorasani K, Sobhani-Tehrani E. Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach. Berlin/Heidelberg:Springer,2009:23-33.
[36]Li L, Ma L, Khorasani K. A Dynamic Recurrent Neural Network Fault Diagnosis and Isolation Architecture for Satellite's Actuator/Thruster Failures. Second International Symposium on Neural Networks. (ISNN 2005), May 30-June 1, 2005.3:574-583.
[37]李智斌,郝永波,涂俊峰等.基于嵌入式仿真的卫星姿控系统故障模拟平台.山东大学学报：工学版,2005,35(3)：88-92.
[38]涂俊峰,李智斌,邢琰.卫星姿态控制故障诊断与系统重构仿真框架.控制工程,2003,10(1)：40-42.
[39]Wu Q, Saif M. Robust fault diagnosis for a satellite system using a neural sliding mode observer. The 44th IEEE Conference on Decision and Control, and the European Control Conference. (CDC-ECC'05), December 12-15,2005: 7668-7673.
[40]Li Z, Ma L, Khorasani K. Fault detection in reaction wheel of a satellite using observer-based dynamic neural networks. Second International Symposium on Neural Networks. (ISNN 2005), May 30-June 1,2005.3:584-590.
[41]Wu Q, Saif M. Robust fault diagnosis for a satellite large angle attitude system using an iterative neuron PID (INPID) observer.2006 American Control Conference. (ACC2006), June 14-16,2006:5710-5715.
[42]Zhang Q. A new residual generation and evaluation method for detection and isolation of faults in non-linear systems. International Journal of Adaptive Control and Signal Processing,2000,14(7):759-773.
[43]Tudoroiu N, Khorasani K. Fault detection and diagnosis for satellite's attitude control system (ACS) using an interactive multiple model (IMM) approach.2005 IEEE International Conference on Control Applications. (CCA),28-31 Aug. 2005:1287-1292.
[44]Tudoroiu N, Khorasani K. Satellite fault diagnosis using a bank of interacting Kalman filters. IEEE Transactions on Aerospace and Electronic Systems, 2007,43(4):1334-1350.
[45]Tudoroiu N, Khorasani K. Fault detection and diagnosis for reaction wheels of satellite's attitude control system using a bank of Kalman filters. International Symposium on Signals, Circuits and Systems. (ISSCS 2005), July 14-15, 2005:199-202.
[46]Tudoroiu N, Sobhani-Tehrani E, Khorasani K. Interactive bank of unscented kalman filters for fault detection and isolation in reaction wheel actuators of satellite attitude control system.32nd Annual Conference on IEEE Industrial Electronics.(IECON 2006),6-10 Nov.2006:264-269.
[47]Jiang T, Khorasani K, Tafazoli S. Parameter estimation-based fault detection, isolation and recovery for nonlinear satellite models. IEEE Transactions on Control Systems Technology,2008,16(4):799-808.
[48]Jiang T, Khorasani K. A fault detection, isolation and reconstruction strategy for a satellite's attitude control subsystem with redundant reaction wheels.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3146-3152.
[49]Talebi H A, Khorasani K, Tafazoli S. A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems with Application to the Satellite's Attitude Control Subsystem. IEEE Transactions on Neural Networks,2009,20(1):45-60.
[50]Talebi H A, Patel R V. An intelligent fault detection and recovery scheme for reaction wheel actuator of satellite attitude control systems:IEEE International Conference on Control Applications,2006. (CCA'06),4-6 Oct.2006:3282-3287.
[51]Talebi H A, Patel R V. A neural network-based fault detection scheme for satellite attitude control systems.2005 IEEE International Conference on Control Applications. (CCA2005), August 28-31,2005:1293-1298.
[52]Talebi H A, Patel R V, Khorasani K. Fault detection and isolation for uncertain nonlinear systems with application to a satellite reaction wheel actuator.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3140-3145.
[53]Talebi H A, Khorasani K, Tafazoli S. A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem. IEEE Transactions on Neural Networks,2009,20(1):45-60.
[54]Talebi H A, Khorasani K. A robust fault detection and isolation scheme with application to magnetorquer type actuators for satellites.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10, 2007:3165-3170.
[55]Frank P M. Analytical and qualitative model-based fault diagnosis-a survey and some new results. European Journal of Control,1996,2(1):6-28.
[56]周东华,王桂增.第五讲 故障诊断技术综述.化工自动化及仪表,1998,25(001)：58-62.
[57]栾家辉.故障重构技术在卫星姿控系统故障诊断中的应用研究[博士学位论文].哈尔滨工业大学,2006.
[58]Frank P M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy::A survey and some new results. Automatica,1990, 26(3):459-474.
[59]Fong K F, Loh A P, Tan W W. A frequency domain approach for fault detection. International Journal of Control,2008,81(2):264-276.
[60]Ding S X. Model-based Fault Diagnosis Techniques:Design Schemes, Algorithms, and Tools. Springer Verlag,2008.
[61]Simani S, Patton R, Fantuzzi C. Model-based fault diagnosis in dynamic systems using identification techniques. Springer-Verlag New York, Inc. Secaucus, NJ, USA,2003.
[62]Chen J, Patton R. Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers,1999.
[63]Chen W, Saif M. Actuator fault diagnosis for uncertain linear systems using a high-order sliding-mode robust differentiator (HOSMRD). International Journal of Robust and Nonlinear Control,2008,18(4-5):413-426.
[64]Chen W, Saif M. Fault detection and isolation based on novel unknown input observer design. The 2006 American Control Conference. (ACC2006), June 14-16, 2006:5129-5134.
[65]Chen W, Saif M. High-order sliding-mode differentiator based actuator fault diagnosis for linear systems with arbitrary relative degree and unmatched Unknown Inputs. the 45th IEEE Conference on Decision and Control. (CDC2006), December 13-15,2006:1153-1158.
[66]Jiang B, Wang J L, Soh Y C. An adaptive technique for robust diagnosis of faults with independent effects on system outputs. International Journal of Control, 2002,75(11):792-802.
[67]Jiang B, Staroswiecki M, Cocquempot V. Fault diagnosis based on adaptive observer for a class of non-linear systems with unknown parameters. International Journal of Control,2004,77(4):367-383.
[68]Jiang B, Staroswiecki M. Adaptive observer design for robust fault estimation. International Journal of Systems Science,2002,33(9):767-775.
[69]Jiang B, Staroswiecki M, Cocquempot V. Fault accommodation for nonlinear dynamic systems. IEEE Transactions on Automatic Control, 2006,51 (9):1578-1583.
[70]Jiang B, Chowdhury F. Observer-based fault diagnosis for a class of nonlinear systems. The 2004 American Control Conference. (ACC2004),30 June-2 July 2004:5671-5675.
[71]金磊,徐世杰.基于扩张状态观测器的飞轮故障检测与恢复.北京航空航天大学学报,2008,34(011)：1272-1275.
[72]王小丽,倪茂林.基于自适应观测器的非线性系统故障诊断.空间控制技术与应用,2008,34(4)：33-37,46.
[73]Zhang X, Parisini T, Polycarpou M M, et al. Adaptive fault-tolerant control of nonlinear uncertain systems:an information-based diagnostic approach. IEEE Transactions on Automatic Control,2004,49(8):1259-1274.
[74]Qu Z, Ihlefeld C M, Jin Y, et al. Robust fault-tolerant self-recovering control of nonlinear uncertain systems. Automatica,2003,39(10):1763-1771.
[75]Tehrani E S, Khorasani K, Tafazoli S. Dynamic neural network-based estimator for fault diagnosis in reaction wheel actuator of satellite attitude control system. The International Joint Conference on Neural Networks. (IJCNN 2005), July 31-August 4,2005:2347-2352.
[76]郭玉英,姜斌,张友民等.基于多模型的飞控系统执行器故障调节.宇航学报,2009,30(2).
[77]Boskovic J D, Bergstrom S E, Mehra R K. Adaptive accommodation of failures in second-order flight control actuators with measurable rates. the 2005 American Control Conference. (ACC2005), June 8-10,2005:1033-1038.
[78]Boskovic J D, Mehra R K. Multiple-model adaptive flight control scheme for accommodation of actuator failures. Journal of Guidance, Control, and Dynamics, 2002,25(4):712-724.
[79]Bernstein D S, Michel A N. A CHRONOLOGICAL BIBLIOGRAPHY ON SATURATING ACTUATORS. International Journal of Robust Nonlinear Control, 1995,5:375-380.
[80]Cao Y Y, Lin Z. Stability analysis of discrete-time systems with actuator saturation by a saturation-dependent Lyapunov function. Automatica,2003,39(7):1235-1241.
[81]Ioannou P A, Sun J. Robust adaptive control. NJ:Prentice Hall Englewood Cliffs, 1996.
[82]Wang H, Daley S. Actuator fault diagnosis:an adaptive observer-based technique. IEEE Transactions on Automatic Control,1996,41(7):1073-1078.
[83]Wang H, Huang Z J, Daley S. On the use of adaptive updating rules for actuator and sensor fault diagnosis. Automatica,1997,33(2):217-225.
[84]廖晖.对地定向三轴稳定卫星姿态确定和控制系统研究[博士学位论文].西安：西北工业大学,2000.
[85]Yi H, Jingqing H. Analysis and design for the second order nonlinear continuous extended states observer. Chinese Science Bulletin,2000,45(21):1938-1944.
[86]Kristiansen R, Nicklasson P J. Satellite attitude control by quaternion-based backstepping. The 2005 American Control Conference. (ACC2005), June 8-10, 2005:907-912.
[87]唐小静,谢琳.基于容错观测器的容错控制系统集成设计.西北工业大学学报,2001,19(2)：313-316.
[88]Gao Z, Ding S X. Fault estimation and fault-tolerant control for descriptor systems via proportional, multiple-integral and derivative observer design. Control Theory & Applications, IET,2007,1(5):1208-1218.
[89]王小丽,倪茂林.基于自适应观测器的非线性系统故障诊断.空间控制技术与应用,2008,34(4)：33-37.
[90]江耿丰,邢琰,王南华.利用奉献观测器诊断红外地球敏感器故障的新方法.航天控制,2007,25(3)：38-42.
[91]宋立辉,姜兴渭.容错降维观测器设计及其应用.哈尔滨工业大学学报,2002,34(5)：618-619.
[92]胡寿松.自动控制原理简明教程.科学出版社,2003.
[93]Larson E C, Parker Jr B E, Clark B R. Model-based sensor and actuator fault detection and isolation.2002 American Control Conference. (ACC2002), May 8-10,2002:4215-4219.
[94]J L, R M. On Fault-tolerant observer. IEEE Transaction on Automatic Control, 1990(35):623-627.
[95]Fortmann T, Williamson D. Design of low-order observers for linear feedback control laws. Automatic Control, IEEE Transactions on,2002,17(3):301-308.
[96]Shousong H, Zhiquan W, Weili H, et al. On fault-tolerant functional observer. the 1994 American Control Conference.(ACC1994),29 June-1 July 1994:260-264.
[97]屠善澄.卫星姿态动力学与控制.宇航出版社,2001.
[98]Tafazoli S, Khorasani K. Nonlinear control and stability analysis of spacecraft attitude recovery. Aerospace and Electronic Systems, IEEE Transactions on, 2006,42(3):825-845.
[99]Isermann R. Fault-diagnosis systems:an introduction from fault detection to fault tolerance. Springer Verlag,2006.
[100]Sobhani-Tehrani E, Khorasani K. Fault diagnosis of nonlinear systems using a hybrid approach. Springer Verlag,2009.
[101]江耿丰,邢琰,王南华.利用特征结构指定隔离卫星滚动偏航陀螺故障的新方法.宇航学报,2007,28(3)：557-561.
[102]江耿丰,邢琰,王南华.利用奉献观测器诊断红外地球敏感器故障的新方法.航天控制,2007,25(3)：38-42.
[103]王璐,潘紫微,叶金杰.基于EKF训练的RBF神经网络及其故障诊断应用.振动.测试与诊断,2008,28(4)：358-361.
[104]魏秀业,潘宏侠,马清峰.粒子群优化的神经网络在故障诊断中的应用.振动.测试与诊断,2006,26(2)：133-137.
[105]Talebi H A, Khorasani K, Tafazoli S. A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite's attitude control subsystem. IEEE Transactions on Neural Networks, 2009,20(1):45-60.
[106]Li Z Q, Ma L, Khorasani K. Fault diagnosis of an actuator in the attitude control subsystem of a satellite using neural networks.2007 International Joint Conference on Neural Networks. (IJCNN 2007), August 12-17,2007:2658-2663.
[107]Li Z, Ma L, Khorasani K. Dynamic neural network-based fault diagnosis for attitude control subsystem of a satellite. PRICAI 2006:Trends in Artificial Intelligence,2006:308-318.
[108]Hagan M T, Demuth H B, Beale M H. Neural network design. PWS Boston, MA, 1996.
[109]蒋睿,魏蛟龙,岑朝辉.基于四元数反馈的卫星姿态控制系统仿真模型建立.系统仿真学报,2009(019)：6260-6265.
[110]Huang B Q, Rashid T, Kechadi T. A new modified network based on the Elman network:Artificial Intelligence and Applications. the IASTED International Conference on Artificial Intelligence and Applications,2004. Febrary 16-18, 2004:379-384.
[111]张恩东,黄文浩.基于小波变换和Kalman滤波的语音增强方法.模式识别与人工智能,2009,22(1)：28-31.
[112]姜连祥,李华旺,杨根庆等.航天器自主故障诊断技术研究进展.宇航学报,2009,30(4)：1320-1326.
[113]Chen S, Su H, Zhang R, et al. Fusing remote sensing images using trous wavelet transform and empirical mode decomposition. Pattern Recognition letters, 2008,29(3):330-342.
[114]周东华,叶银忠.现代故障诊断与容错控制.清华大学出版社,2000.
[115]Ayaz E, Ztiirk A, Seker S, et al. Fault detection based on continuous wavelet transform and sensor fusion in electric motors. COMPEL:The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2009,28(2):454-470.
[116]Norgaard M. NNSID Toolbox.1997.
[117]Mandic D P, Chambers J A. Recurrent neural networks for prediction:Learning algorithms, architectures and stability. Wiley,2001.
[118]Sj Berg J, Zhang Q, Ljung L, et al. Nonlinear black-box modeling in system identification:a unified overview. Automatica,1995,31(12):1691-1724.
[119]Chow T, Li X D. Modeling of continuous time dynamical systems with input by recurrent neural networks. Circuits and Systems I:Fundamental Theory and Applications, IEEE Transactions on,2002,47(4):575-578.
[120]Hornik K, Stinchcombe M, White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural networks,1990,3(5):551-560.
[121]余涌涛,梁加红.基于SIMULINK的卫星姿控系统的仿真实现.计算机仿真,2006,23(11)：71-74.
[122]王宁强,刘向东,陈振等.基于MATLAB的卫星姿态控制半物理实时仿真平台.系统仿真学报,2005,7(7)：1617-1620.
[123]伏洪勇,林宝军,杨新.基于HLA/pRTI的卫星姿轨控系统仿真研究.系统仿真学报,2006,18(3)：768-770.
[124]翟坤,杨涤,朱承元等.基于dSPACE的挠性卫星姿轨控实时仿真系统.航天控制,2004,22(1)：17-25.
[125]张子龙,朱庆华,杨涤等.基于xPC卫星系统硬件在回路实时仿真研究.系统仿真学报,2005,17(2)：61-63.
[126]杨涤,李立涛,杨旭等.系统实时仿真开发环境与应用.北京：清华大学出版社,2002.
[127]李季苏,牟小刚,张锦江.卫星控制系统全物理仿真.航天控制,2004,22(2)：37-41.
[128]刘莹莹,周军,孙剑.卫星多轴指向姿态控制全物理仿真实验研究.宇航学报,2006,27(4)：790-793.
[129]刘慎钊.卫星控制系统多转台多模拟器半物理仿真方法.航天控制,2004,22(4)：73-77.
[130]李智斌,郝永波,涂俊峰等.基于嵌入式仿真的卫星姿控系统故障模拟平台.山东大学学报：工学版,2005,35(3)：88-92.
[2]邢琰,吴宏鑫,王晓磊等.航天器故障诊断与容错控制技术综述.宇航学报,2003,24(3)：221-226.
[3]张宗美.航天故障手册.宇航出版社,1994.
[4]姜连祥,李华旺,杨根庆等.航天器自主故障诊断技术研究进展.宇航学报,2009,30(4)：1320-1326.
[5]Talebi H A, Patel R V, Khorasani K. Fault detection and isolation for uncertain nonlinear systems with application to a satellite reaction wheel actuator.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3140-3145.
[6]Talebi H A, Khorasani K. A robust fault detection and isolation scheme with application to magnetorquer type actuators for satellites.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007: 3165-3170.
[7]Li L, Ma L, Khorasani K. A Dynamic Recurrent Neural Network Fault Diagnosis and Isolation Architecture for Satellite's Actuator/Thruster Failures. Second International Symposium on Neural Networks. (ISNN 2005), May 30-June 1, 2005.3:574-583.
[8]Al-Zyoud I, Khorasani K. Neural Network-based Actuator Fault Diagnosis for Attitude Control Subsystem of a Satellite.2006 World Automation Congres. (WAC'06), June 24-June 26,2006:1-6.
[9]Al-Zyoud A D, Khorasani K. Neural network-based actuator fault diagnosis for attitude control subsystem of an unmanned space vehicle. International Joint Conference on Neural Networks 2006. (IJCNN'06), July 16-July 21,2006: 3686-3693.
[10]Li Z Q, Ma L, Khorasani K. A dynamic neural network-based reaction wheel fault diagnosis for satellites:Neural Networks. International Joint Conference on Neural Networks 2006. (IJCNN'06), July 16-July 21,2006:3714-3721.
[11]Jiang T, Khorasani K. A fault detection, isolation and reconstruction strategy for a satellite's attitude control subsystem with redundant reaction wheels.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3146-3152.
[12]Saif M, Chen W, Wu Q. High Order Sliding Mode Observers and Differentiators-Application to Fault Diagnosis Problem. Modern Sliding Mode Control Theory,2008:321-344.
[13]Tudoroiu N, Khorasani K. Fault detection and diagnosis for satellite's attitude control system (ACS) using an interactive multiple model (IMM) approach.2005 IEEE International Conference on Control Applications. (CCA),28-31 Aug.2005: 1287-1292.
[14]陈雪芹,耿云海,张世杰等.基于混合H2H∞的集成故障诊断与容错控制研究.宇航学报,2007,28(4)：890-896.
[15]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.系统工程与电子技术,2007,29(5)：774-777.
[16]荣吉利.基于模型的航天器在轨传感器故障诊断方法.兵工学报,2002,23(2)：242-245.
[17]段晨阳,汤国建,张世杰.单轴飞轮故障时的小卫星姿态控制方法研究.航天控制,2007,25(3)：48-52.
[18]吴丽娜.卫星姿态控制系统故障诊断研究[硕士学位论文].哈尔滨工业大学,2006.
[19]Williams B C, Nayak P P. A model-based approach to reactive self-configuring systems. The Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference.4-8 Aug. 1996.2:971-978.
[20]Bernard D E, Gamble E B, Rouquette N F, et al. Remote Agent Experiment DS1 Technology Validation Report:Ames Research Center and JPL[Report].2000.
[21]Meyer C M, Fulton C, Maul B, et al. Propulsion IVHM Technology Experiment Overview. IEEE Aerospace Conference 2003. March 8,2003:859-868.
[22]Hayden S, Sweet A, Christa S E. Livingstone model-based diagnosis of Earth Observing One. Collection of Technical Papers-AIAA 1st Intelligent Systems Technical Conference, September 20-23,2004:98-108.
[23]Hayden S C, Sweet A J, Christa S E, et al. Advanced Diagnostic System on Earth Observing One. A Collection of Technical Papers-AIAA Space 2004 Conference and Exposition. September 28-30,2004:2209-2222.
[24]Hayden S C, Sweet A J, Shulman S. Lessons learned in the Livingstone 2 on Earth Observing One flight experiment. Info Tech at Aerospace:Advancing Contemporary Aerospace Technologies and Their Integration. September 26-29, 2005:863-877.
[25]M S, J S, L B. The NASA integrated vehicle health management techonology experiment for X-37. the SPIE AeroSense 2002 Symposium, Florida, April 3, 2002:1-12.
[26]刘洪刚,吴建军,陈启智.基于模型知识的液体火箭发动机故障诊断方法研究.宇航学报,2002,23(2)：41-43.
[27]郑威,吴建军.液体火箭发动机基于定性键合图模型的故障诊断方法研究.宇航学报,2004,25(6)：604-608.
[28]朱永娇,刘洪刚.基于定性模型和定量知识集成的智能故障诊断方法研究.科学技术与工程,2007,7(13)：3107-3110.
[29]彭蓉,秦永元.基于多敏感器卫星姿态确定系统故障检测方法研究.机械强度, 2007,29(3)：487-491.
[30]姚敏,赵敏.小卫星多级故障诊断系统设计.中国空间科学技术,2007,27(2)：47-51.
[31]杨家军,马兴瑞.用模糊聚类算法快速定位小卫星在轨运行故障.空间科学学报,1999,19(4)：354-361.
[32]Mohammadi R, Hashtrudi-Zad S, Khorasani K. A hybrid architecture for diagnosis in hybrid systems with applications to spacecraft propulsion system.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3184-3190.
[33]Tansel I N, Li M, Yapiei A, et al. Integrated Systems Health Monitoring for Autonomous Space Access Vehicles and Satellites. The 3rd International Conference on Recent Advances in Space Technologies. (RAST 2007), June 14-16, 2007:187-192.
[34]Yairi T, Kawahara Y, Fujimaki R, et al. Telemetry-mining:a machine learning approach to anomaly detection and fault diagnosis for space systems.2nd IEEE International Conference on Space Mission Challenges for Information Technology. (SMC-IT 2006), July 17-20,2006:466-473.
[35]Khorasani K, Sobhani-Tehrani E. Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach. Berlin/Heidelberg:Springer,2009:23-33.
[36]Li L, Ma L, Khorasani K. A Dynamic Recurrent Neural Network Fault Diagnosis and Isolation Architecture for Satellite's Actuator/Thruster Failures. Second International Symposium on Neural Networks. (ISNN 2005), May 30-June 1, 2005.3:574-583.
[37]李智斌,郝永波,涂俊峰等.基于嵌入式仿真的卫星姿控系统故障模拟平台.山东大学学报：工学版,2005,35(3)：88-92.
[38]涂俊峰,李智斌,邢琰.卫星姿态控制故障诊断与系统重构仿真框架.控制工程,2003,10(1)：40-42.
[39]Wu Q, Saif M. Robust fault diagnosis for a satellite system using a neural sliding mode observer. The 44th IEEE Conference on Decision and Control, and the European Control Conference. (CDC-ECC'05), December 12-15,2005: 7668-7673.
[40]Li Z, Ma L, Khorasani K. Fault detection in reaction wheel of a satellite using observer-based dynamic neural networks. Second International Symposium on Neural Networks. (ISNN 2005), May 30-June 1,2005.3:584-590.
[41]Wu Q, Saif M. Robust fault diagnosis for a satellite large angle attitude system using an iterative neuron PID (INPID) observer.2006 American Control Conference. (ACC2006), June 14-16,2006:5710-5715.
[42]Zhang Q. A new residual generation and evaluation method for detection and isolation of faults in non-linear systems. International Journal of Adaptive Control and Signal Processing,2000,14(7):759-773.
[43]Tudoroiu N, Khorasani K. Fault detection and diagnosis for satellite's attitude control system (ACS) using an interactive multiple model (IMM) approach.2005 IEEE International Conference on Control Applications. (CCA),28-31 Aug. 2005:1287-1292.
[44]Tudoroiu N, Khorasani K. Satellite fault diagnosis using a bank of interacting Kalman filters. IEEE Transactions on Aerospace and Electronic Systems, 2007,43(4):1334-1350.
[45]Tudoroiu N, Khorasani K. Fault detection and diagnosis for reaction wheels of satellite's attitude control system using a bank of Kalman filters. International Symposium on Signals, Circuits and Systems. (ISSCS 2005), July 14-15, 2005:199-202.
[46]Tudoroiu N, Sobhani-Tehrani E, Khorasani K. Interactive bank of unscented kalman filters for fault detection and isolation in reaction wheel actuators of satellite attitude control system.32nd Annual Conference on IEEE Industrial Electronics.(IECON 2006),6-10 Nov.2006:264-269.
[47]Jiang T, Khorasani K, Tafazoli S. Parameter estimation-based fault detection, isolation and recovery for nonlinear satellite models. IEEE Transactions on Control Systems Technology,2008,16(4):799-808.
[48]Jiang T, Khorasani K. A fault detection, isolation and reconstruction strategy for a satellite's attitude control subsystem with redundant reaction wheels.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3146-3152.
[49]Talebi H A, Khorasani K, Tafazoli S. A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems with Application to the Satellite's Attitude Control Subsystem. IEEE Transactions on Neural Networks,2009,20(1):45-60.
[50]Talebi H A, Patel R V. An intelligent fault detection and recovery scheme for reaction wheel actuator of satellite attitude control systems:IEEE International Conference on Control Applications,2006. (CCA'06),4-6 Oct.2006:3282-3287.
[51]Talebi H A, Patel R V. A neural network-based fault detection scheme for satellite attitude control systems.2005 IEEE International Conference on Control Applications. (CCA2005), August 28-31,2005:1293-1298.
[52]Talebi H A, Patel R V, Khorasani K. Fault detection and isolation for uncertain nonlinear systems with application to a satellite reaction wheel actuator.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10,2007:3140-3145.
[53]Talebi H A, Khorasani K, Tafazoli S. A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem. IEEE Transactions on Neural Networks,2009,20(1):45-60.
[54]Talebi H A, Khorasani K. A robust fault detection and isolation scheme with application to magnetorquer type actuators for satellites.2007 IEEE International Conference on Systems, Man, and Cybernetics. (SMC 2007), October 7-10, 2007:3165-3170.
[55]Frank P M. Analytical and qualitative model-based fault diagnosis-a survey and some new results. European Journal of Control,1996,2(1):6-28.
[56]周东华,王桂增.第五讲 故障诊断技术综述.化工自动化及仪表,1998,25(001)：58-62.
[57]栾家辉.故障重构技术在卫星姿控系统故障诊断中的应用研究[博士学位论文].哈尔滨工业大学,2006.
[58]Frank P M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy::A survey and some new results. Automatica,1990, 26(3):459-474.
[59]Fong K F, Loh A P, Tan W W. A frequency domain approach for fault detection. International Journal of Control,2008,81(2):264-276.
[60]Ding S X. Model-based Fault Diagnosis Techniques:Design Schemes, Algorithms, and Tools. Springer Verlag,2008.
[61]Simani S, Patton R, Fantuzzi C. Model-based fault diagnosis in dynamic systems using identification techniques. Springer-Verlag New York, Inc. Secaucus, NJ, USA,2003.
[62]Chen J, Patton R. Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers,1999.
[63]Chen W, Saif M. Actuator fault diagnosis for uncertain linear systems using a high-order sliding-mode robust differentiator (HOSMRD). International Journal of Robust and Nonlinear Control,2008,18(4-5):413-426.
[64]Chen W, Saif M. Fault detection and isolation based on novel unknown input observer design. The 2006 American Control Conference. (ACC2006), June 14-16, 2006:5129-5134.
[65]Chen W, Saif M. High-order sliding-mode differentiator based actuator fault diagnosis for linear systems with arbitrary relative degree and unmatched Unknown Inputs. the 45th IEEE Conference on Decision and Control. (CDC2006), December 13-15,2006:1153-1158.
[66]Jiang B, Wang J L, Soh Y C. An adaptive technique for robust diagnosis of faults with independent effects on system outputs. International Journal of Control, 2002,75(11):792-802.
[67]Jiang B, Staroswiecki M, Cocquempot V. Fault diagnosis based on adaptive observer for a class of non-linear systems with unknown parameters. International Journal of Control,2004,77(4):367-383.
[68]Jiang B, Staroswiecki M. Adaptive observer design for robust fault estimation. International Journal of Systems Science,2002,33(9):767-775.
[69]Jiang B, Staroswiecki M, Cocquempot V. Fault accommodation for nonlinear dynamic systems. IEEE Transactions on Automatic Control, 2006,51 (9):1578-1583.
[70]Jiang B, Chowdhury F. Observer-based fault diagnosis for a class of nonlinear systems. The 2004 American Control Conference. (ACC2004),30 June-2 July 2004:5671-5675.
[71]金磊,徐世杰.基于扩张状态观测器的飞轮故障检测与恢复.北京航空航天大学学报,2008,34(011)：1272-1275.
[72]王小丽,倪茂林.基于自适应观测器的非线性系统故障诊断.空间控制技术与应用,2008,34(4)：33-37,46.
[73]Zhang X, Parisini T, Polycarpou M M, et al. Adaptive fault-tolerant control of nonlinear uncertain systems:an information-based diagnostic approach. IEEE Transactions on Automatic Control,2004,49(8):1259-1274.
[74]Qu Z, Ihlefeld C M, Jin Y, et al. Robust fault-tolerant self-recovering control of nonlinear uncertain systems. Automatica,2003,39(10):1763-1771.
[75]Tehrani E S, Khorasani K, Tafazoli S. Dynamic neural network-based estimator for fault diagnosis in reaction wheel actuator of satellite attitude control system. The International Joint Conference on Neural Networks. (IJCNN 2005), July 31-August 4,2005:2347-2352.
[76]郭玉英,姜斌,张友民等.基于多模型的飞控系统执行器故障调节.宇航学报,2009,30(2).
[77]Boskovic J D, Bergstrom S E, Mehra R K. Adaptive accommodation of failures in second-order flight control actuators with measurable rates. the 2005 American Control Conference. (ACC2005), June 8-10,2005:1033-1038.
[78]Boskovic J D, Mehra R K. Multiple-model adaptive flight control scheme for accommodation of actuator failures. Journal of Guidance, Control, and Dynamics, 2002,25(4):712-724.
[79]Bernstein D S, Michel A N. A CHRONOLOGICAL BIBLIOGRAPHY ON SATURATING ACTUATORS. International Journal of Robust Nonlinear Control, 1995,5:375-380.
[80]Cao Y Y, Lin Z. Stability analysis of discrete-time systems with actuator saturation by a saturation-dependent Lyapunov function. Automatica,2003,39(7):1235-1241.
[81]Ioannou P A, Sun J. Robust adaptive control. NJ:Prentice Hall Englewood Cliffs, 1996.
[82]Wang H, Daley S. Actuator fault diagnosis:an adaptive observer-based technique. IEEE Transactions on Automatic Control,1996,41(7):1073-1078.
[83]Wang H, Huang Z J, Daley S. On the use of adaptive updating rules for actuator and sensor fault diagnosis. Automatica,1997,33(2):217-225.
[84]廖晖.对地定向三轴稳定卫星姿态确定和控制系统研究[博士学位论文].西安：西北工业大学,2000.
[85]Yi H, Jingqing H. Analysis and design for the second order nonlinear continuous extended states observer. Chinese Science Bulletin,2000,45(21):1938-1944.
[86]Kristiansen R, Nicklasson P J. Satellite attitude control by quaternion-based backstepping. The 2005 American Control Conference. (ACC2005), June 8-10, 2005:907-912.
[87]唐小静,谢琳.基于容错观测器的容错控制系统集成设计.西北工业大学学报,2001,19(2)：313-316.
[88]Gao Z, Ding S X. Fault estimation and fault-tolerant control for descriptor systems via proportional, multiple-integral and derivative observer design. Control Theory & Applications, IET,2007,1(5):1208-1218.
[89]王小丽,倪茂林.基于自适应观测器的非线性系统故障诊断.空间控制技术与应用,2008,34(4)：33-37.
[90]江耿丰,邢琰,王南华.利用奉献观测器诊断红外地球敏感器故障的新方法.航天控制,2007,25(3)：38-42.
[91]宋立辉,姜兴渭.容错降维观测器设计及其应用.哈尔滨工业大学学报,2002,34(5)：618-619.
[92]胡寿松.自动控制原理简明教程.科学出版社,2003.
[93]Larson E C, Parker Jr B E, Clark B R. Model-based sensor and actuator fault detection and isolation.2002 American Control Conference. (ACC2002), May 8-10,2002:4215-4219.
[94]J L, R M. On Fault-tolerant observer. IEEE Transaction on Automatic Control, 1990(35):623-627.
[95]Fortmann T, Williamson D. Design of low-order observers for linear feedback control laws. Automatic Control, IEEE Transactions on,2002,17(3):301-308.
[96]Shousong H, Zhiquan W, Weili H, et al. On fault-tolerant functional observer. the 1994 American Control Conference.(ACC1994),29 June-1 July 1994:260-264.
[97]屠善澄.卫星姿态动力学与控制.宇航出版社,2001.
[98]Tafazoli S, Khorasani K. Nonlinear control and stability analysis of spacecraft attitude recovery. Aerospace and Electronic Systems, IEEE Transactions on, 2006,42(3):825-845.
[99]Isermann R. Fault-diagnosis systems:an introduction from fault detection to fault tolerance. Springer Verlag,2006.
[100]Sobhani-Tehrani E, Khorasani K. Fault diagnosis of nonlinear systems using a hybrid approach. Springer Verlag,2009.
[101]江耿丰,邢琰,王南华.利用特征结构指定隔离卫星滚动偏航陀螺故障的新方法.宇航学报,2007,28(3)：557-561.
[102]江耿丰,邢琰,王南华.利用奉献观测器诊断红外地球敏感器故障的新方法.航天控制,2007,25(3)：38-42.
[103]王璐,潘紫微,叶金杰.基于EKF训练的RBF神经网络及其故障诊断应用.振动.测试与诊断,2008,28(4)：358-361.
[104]魏秀业,潘宏侠,马清峰.粒子群优化的神经网络在故障诊断中的应用.振动.测试与诊断,2006,26(2)：133-137.
[105]Talebi H A, Khorasani K, Tafazoli S. A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite's attitude control subsystem. IEEE Transactions on Neural Networks, 2009,20(1):45-60.
[106]Li Z Q, Ma L, Khorasani K. Fault diagnosis of an actuator in the attitude control subsystem of a satellite using neural networks.2007 International Joint Conference on Neural Networks. (IJCNN 2007), August 12-17,2007:2658-2663.
[107]Li Z, Ma L, Khorasani K. Dynamic neural network-based fault diagnosis for attitude control subsystem of a satellite. PRICAI 2006:Trends in Artificial Intelligence,2006:308-318.
[108]Hagan M T, Demuth H B, Beale M H. Neural network design. PWS Boston, MA, 1996.
[109]蒋睿,魏蛟龙,岑朝辉.基于四元数反馈的卫星姿态控制系统仿真模型建立.系统仿真学报,2009(019)：6260-6265.
[110]Huang B Q, Rashid T, Kechadi T. A new modified network based on the Elman network:Artificial Intelligence and Applications. the IASTED International Conference on Artificial Intelligence and Applications,2004. Febrary 16-18, 2004:379-384.
[111]张恩东,黄文浩.基于小波变换和Kalman滤波的语音增强方法.模式识别与人工智能,2009,22(1)：28-31.
[112]姜连祥,李华旺,杨根庆等.航天器自主故障诊断技术研究进展.宇航学报,2009,30(4)：1320-1326.
[113]Chen S, Su H, Zhang R, et al. Fusing remote sensing images using trous wavelet transform and empirical mode decomposition. Pattern Recognition letters, 2008,29(3):330-342.
[114]周东华,叶银忠.现代故障诊断与容错控制.清华大学出版社,2000.
[115]Ayaz E, Ztiirk A, Seker S, et al. Fault detection based on continuous wavelet transform and sensor fusion in electric motors. COMPEL:The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2009,28(2):454-470.
[116]Norgaard M. NNSID Toolbox.1997.
[117]Mandic D P, Chambers J A. Recurrent neural networks for prediction:Learning algorithms, architectures and stability. Wiley,2001.
[118]Sj Berg J, Zhang Q, Ljung L, et al. Nonlinear black-box modeling in system identification:a unified overview. Automatica,1995,31(12):1691-1724.
[119]Chow T, Li X D. Modeling of continuous time dynamical systems with input by recurrent neural networks. Circuits and Systems I:Fundamental Theory and Applications, IEEE Transactions on,2002,47(4):575-578.
[120]Hornik K, Stinchcombe M, White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural networks,1990,3(5):551-560.
[121]余涌涛,梁加红.基于SIMULINK的卫星姿控系统的仿真实现.计算机仿真,2006,23(11)：71-74.
[122]王宁强,刘向东,陈振等.基于MATLAB的卫星姿态控制半物理实时仿真平台.系统仿真学报,2005,7(7)：1617-1620.
[123]伏洪勇,林宝军,杨新.基于HLA/pRTI的卫星姿轨控系统仿真研究.系统仿真学报,2006,18(3)：768-770.
[124]翟坤,杨涤,朱承元等.基于dSPACE的挠性卫星姿轨控实时仿真系统.航天控制,2004,22(1)：17-25.
[125]张子龙,朱庆华,杨涤等.基于xPC卫星系统硬件在回路实时仿真研究.系统仿真学报,2005,17(2)：61-63.
[126]杨涤,李立涛,杨旭等.系统实时仿真开发环境与应用.北京：清华大学出版社,2002.
[127]李季苏,牟小刚,张锦江.卫星控制系统全物理仿真.航天控制,2004,22(2)：37-41.
[128]刘莹莹,周军,孙剑.卫星多轴指向姿态控制全物理仿真实验研究.宇航学报,2006,27(4)：790-793.
[129]刘慎钊.卫星控制系统多转台多模拟器半物理仿真方法.航天控制,2004,22(4)：73-77.
[130]李智斌,郝永波,涂俊峰等.基于嵌入式仿真的卫星姿控系统故障模拟平台.山东大学学报：工学版,2005,35(3)：88-92.
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