LPV系统的鲁棒故障估计与主动容错控制
|
摘要
动态系统的安全性、可靠性和对环境的污染问题日益受到人们的重视,这些要求不仅仅局限于核反应堆、化工过程、飞行器等安全性很重要的系统,而且已经扩展到诸如自主车辆、快速轨道交通等新型系统。故障诊断与容错控制是提高自动化系统可靠性与安全性的有效途径。因此深入研究故障诊断与容错控制技术,不但具有重要的理论意义,而且具有很大的实际应用价值。由于建模不确定性和未知扰动的广泛存在,近年来鲁棒故障诊断也受到了广泛关注。
线性参数变化(LPV)系统是一类特殊的线性系统,其状态空间矩阵(系统状态空间描述中的系数矩阵,简称“状态空间矩阵”)是某些时变参数向量的函数。当这些时变参数沿某给定的参数轨迹变化时,LPV系统就退化为一般的线性时变(LTV)系统;当这些参数为某固定值时,系统退化为线性定常(LTI)系统。而且从实际应用的角度来看,通过沿参数轨迹进行线性化可以将一大类非线性系统转化为LPV系统。因此,有必要研究LPV系统的鲁棒故障诊断与容错控制。目前关于LPV系统的鲁棒故障诊断与容错控制已经引起了广泛关注,成为控制界的热门研究课题之一。
本文重点研究了一类LPV系统的鲁棒故障估计、时滞LPV系统的鲁棒故障估计、LPV系统的鲁棒控制器与鲁棒故障估计器的联合设计、以及基于H∞增益调度技术的主动容错控制问题。本文的创新点主要有以下几点:
(1)针对一类LPV系统,提出了一种鲁棒H∞故障估计器的设计方法。这类系统的特点是其状态空间矩阵仿射依赖于时变参数向量。本文首先将鲁棒故障估计问题转化为鲁棒H∞控制问题。然后基于增益调度思想和LPV系统的有界实引理,以线性矩阵不等式的形式给出了LPV故障估计器存在的充分必要条件,并给出了自调度LPV故障估计器的详细构造方法,所构造的LPV故障估计器与被检测对象具有相同的参数依赖关系。最后通过计算机仿真验证了该方法的有效性。
(2)针对一类时滞LPV系统,提出了一种鲁棒故障估计器的设计方法。这类系统的特点是其状态空间矩阵仿射依赖于一组可实时测量的时变参数,系统中存在未知的状态时滞,且时滞的变化率有界。基于H∞控制理论和时滞LPV系统的有界实引理,以线性矩阵不等式为工具,推导了自调度LPV故障估计器存在的充分条件,并给出了一种与被检测对象具有相同参数依赖关系的鲁棒LPV故障估计器的构造方法,所构造的故障估计器能够有效、快速地跟踪故障信号,并且对状态时滞和外界不确定扰动具有良好的鲁棒性。
(3)针对一类仿射参数依赖的不确定LPV系统,提出了一种鲁棒控制器/故障估计器的联合设计方法,即同时设计鲁棒控制器和故障估计器。首先通过引入故障估计误差,将联合设计问题转化为H∞控制问题,再引入虚拟性能块,将鲁棒控制问题转化为鲁棒稳定性问题。然后基于定标H∞理论,给出了LPV系统的定标有界实引理,并在此基础上导出了LPV联合“控制器/故障估计器”存在的充分条件。当条件满足时,给出了该联合“控制器/故障估计器”的构造方法。所构造的LPV联合“控制器/故障估计器”与对象具有相同的参数依赖关系,且能够同时给出鲁棒控制信号和故障估计信号。最后针对一个具有调节器故障的不确定系统进行了仿真研究,验证了方法的有效性。
(4)基于增益调度H∞设计策略提出了一种新的主动容错控制方案。首先针对同时出现传感器故障、部件故障和调节器故障的情形,建立了故障系统的LPV模型。然后在假设故障对系统矩阵的影响可以表示为仿射参数依赖的前提下,基于增益调度H∞理论设计了一种可重构的鲁棒LPV控制器,该控制器为故障影响因子的函数,即控制器的调度变量为故障影响因子,可以利用故障检测与分离机构所产生的残差来对故障影响因子进行在线估计。为了验证所提出的主动容错策略的有效性,针对两级倒立摆系统中的电机测速回路故障问题,设计了主动容错控制器。
There is an increasing demand for dynamic systems to become safer, more reliable and less polluting to the environment. These requirements extend beyond normally accepted safety-critical systems of nuclear reactors, chemical plants or aircraft, to new systems such as autonomous vehicles or fast rail systems. An effective way to improve the safety and dependability in automated systems is to introduce fault diagnosis and fault-tolerant control. Hence, the study on fault diagnosis and fault-tolerant control technology has both theoretical and practical importance. Due to the existence of modeling uncertainties and unknown disturbance, robust fault diagnosis has received more and more attention in recent years.
Linear parameter-varying (LPV) systems are special linear plants whose state-space matrices (i.e., the coefficient matrices of the state-space model) are functions of some vector of varying parameters. An LPV system can be reduced to a linear time-varying (LTV) system for a given parameter trajectory, and it can also be transformed into a linear time-invariant (LTI) system on a constant trajectory. From a practical point of view, a large class of nonlinear systems can be reduced to LPV systems by linearization along the trajectories of the parameters. So it is necessary to study the robust fault diagnosis and fault tolerant control for LPV systems. At present, it has drawn wide attention, and has become one of the main topics in control domain.
This dissertation focuses on the robust fault estimation for a class of LPV systems, the robust fault estimation for a class of LPV time-delay systems, the integrated design of robust controller and fault estimator for a class of uncertain LPV systems, and the active fault-tolerant control based on gain-scheduled H∞design strategy. The main contributions of this dissertation can be summarized as follows:
(1) For a class of LPV plants which depend affinely on the vector of time-varying parameters, a robust H∞fault estimator design method is proposed. First, the robust fault estimation problem is transformed into a robust H∞control problem. Then, based on gain-scheduled techniques and bounded real lemma for LPV systems, necessary and sufficient conditions for the existence of LPV fault estimators are presented in terms of linear matrix inequalities (LMIs). When these conditions hold, a method for constructing the self-scheduled LPV estimators is proposed, and the resulting LPV estimators have the same parameter dependence as the plants. Finally, the effectiveness of the proposed method is demonstrated through an example.
(2) For a class of LPV time-delay plants where the state-space matrices depend affinely on time-varying parameters that can be measured in real-time and the time-delay is unknown but with bounded variation rates, a method for designing the robust fault estimator is presented. Based on H∞control theory and bounded real lemma for LPV time-delay systems, sufficient conditions for the existence of the self-scheduled LPV fault estimators are developed in terms of LMIs that can be solved via efficient interior-point algorithms. As these conditions hold, a robust fault estimator which has the same parameter dependence as the plant is constructed, the resulting estimator can follow the fault swiftly and effectively and with robustness to time-delay and exogenous disturbance.
(3) An integrated methd for designing robust controller and fault estimator for a class of uncertain LPV plants which depend affinely on a vector of time-varying parameters is proposed. First, the integrated design problem is reduced to an H∞control problem by introducing the concept of fault estimation error, the resulting robust control problem can be further transformed into a robust stability problem by inserting some fictitious performance blocks. Then, based on scaled H∞theory, a scaled bounded real lemma for LPV systems is proposed. On the basis of this lemma, sufficient conditions for the existence of an LPV integrated“controller/fault estimator”are developed. As these conditions hold, an algorithm for constructing the LPV integrated“controller/fault estimator”is presented. The resulting LPV“controller/fault estimator”has the same parameter dependence as the plants, and can generate both control signals and fault estimations. Finally, to demonstrate the effectiveness of the proposed method, an uncertain system with actuator faults is investigated.
(4) Based on the gain-scheduled H∞design strategy, a novel active fault-tolerant control scheme is proposed. An LPV model for systems with all possible sensor, component and actuator faults is presented. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine parameter dependence, a reconfigurable robust LPV controller is developed based on gain-scheduled H∞theory. The resulting controller is a function of the fault effect factors which can be estimated on-line from the residual vector of the fault detection and isolation (FDI) mechanism. To demonstrate the effectiveness of the proposed method, an active fault tolerant controller is designed for a double inverted pendulum system with a fault in the motor tachometer loop.
线性参数变化(LPV)系统是一类特殊的线性系统,其状态空间矩阵(系统状态空间描述中的系数矩阵,简称“状态空间矩阵”)是某些时变参数向量的函数。当这些时变参数沿某给定的参数轨迹变化时,LPV系统就退化为一般的线性时变(LTV)系统;当这些参数为某固定值时,系统退化为线性定常(LTI)系统。而且从实际应用的角度来看,通过沿参数轨迹进行线性化可以将一大类非线性系统转化为LPV系统。因此,有必要研究LPV系统的鲁棒故障诊断与容错控制。目前关于LPV系统的鲁棒故障诊断与容错控制已经引起了广泛关注,成为控制界的热门研究课题之一。
本文重点研究了一类LPV系统的鲁棒故障估计、时滞LPV系统的鲁棒故障估计、LPV系统的鲁棒控制器与鲁棒故障估计器的联合设计、以及基于H∞增益调度技术的主动容错控制问题。本文的创新点主要有以下几点:
(1)针对一类LPV系统,提出了一种鲁棒H∞故障估计器的设计方法。这类系统的特点是其状态空间矩阵仿射依赖于时变参数向量。本文首先将鲁棒故障估计问题转化为鲁棒H∞控制问题。然后基于增益调度思想和LPV系统的有界实引理,以线性矩阵不等式的形式给出了LPV故障估计器存在的充分必要条件,并给出了自调度LPV故障估计器的详细构造方法,所构造的LPV故障估计器与被检测对象具有相同的参数依赖关系。最后通过计算机仿真验证了该方法的有效性。
(2)针对一类时滞LPV系统,提出了一种鲁棒故障估计器的设计方法。这类系统的特点是其状态空间矩阵仿射依赖于一组可实时测量的时变参数,系统中存在未知的状态时滞,且时滞的变化率有界。基于H∞控制理论和时滞LPV系统的有界实引理,以线性矩阵不等式为工具,推导了自调度LPV故障估计器存在的充分条件,并给出了一种与被检测对象具有相同参数依赖关系的鲁棒LPV故障估计器的构造方法,所构造的故障估计器能够有效、快速地跟踪故障信号,并且对状态时滞和外界不确定扰动具有良好的鲁棒性。
(3)针对一类仿射参数依赖的不确定LPV系统,提出了一种鲁棒控制器/故障估计器的联合设计方法,即同时设计鲁棒控制器和故障估计器。首先通过引入故障估计误差,将联合设计问题转化为H∞控制问题,再引入虚拟性能块,将鲁棒控制问题转化为鲁棒稳定性问题。然后基于定标H∞理论,给出了LPV系统的定标有界实引理,并在此基础上导出了LPV联合“控制器/故障估计器”存在的充分条件。当条件满足时,给出了该联合“控制器/故障估计器”的构造方法。所构造的LPV联合“控制器/故障估计器”与对象具有相同的参数依赖关系,且能够同时给出鲁棒控制信号和故障估计信号。最后针对一个具有调节器故障的不确定系统进行了仿真研究,验证了方法的有效性。
(4)基于增益调度H∞设计策略提出了一种新的主动容错控制方案。首先针对同时出现传感器故障、部件故障和调节器故障的情形,建立了故障系统的LPV模型。然后在假设故障对系统矩阵的影响可以表示为仿射参数依赖的前提下,基于增益调度H∞理论设计了一种可重构的鲁棒LPV控制器,该控制器为故障影响因子的函数,即控制器的调度变量为故障影响因子,可以利用故障检测与分离机构所产生的残差来对故障影响因子进行在线估计。为了验证所提出的主动容错策略的有效性,针对两级倒立摆系统中的电机测速回路故障问题,设计了主动容错控制器。
There is an increasing demand for dynamic systems to become safer, more reliable and less polluting to the environment. These requirements extend beyond normally accepted safety-critical systems of nuclear reactors, chemical plants or aircraft, to new systems such as autonomous vehicles or fast rail systems. An effective way to improve the safety and dependability in automated systems is to introduce fault diagnosis and fault-tolerant control. Hence, the study on fault diagnosis and fault-tolerant control technology has both theoretical and practical importance. Due to the existence of modeling uncertainties and unknown disturbance, robust fault diagnosis has received more and more attention in recent years.
Linear parameter-varying (LPV) systems are special linear plants whose state-space matrices (i.e., the coefficient matrices of the state-space model) are functions of some vector of varying parameters. An LPV system can be reduced to a linear time-varying (LTV) system for a given parameter trajectory, and it can also be transformed into a linear time-invariant (LTI) system on a constant trajectory. From a practical point of view, a large class of nonlinear systems can be reduced to LPV systems by linearization along the trajectories of the parameters. So it is necessary to study the robust fault diagnosis and fault tolerant control for LPV systems. At present, it has drawn wide attention, and has become one of the main topics in control domain.
This dissertation focuses on the robust fault estimation for a class of LPV systems, the robust fault estimation for a class of LPV time-delay systems, the integrated design of robust controller and fault estimator for a class of uncertain LPV systems, and the active fault-tolerant control based on gain-scheduled H∞design strategy. The main contributions of this dissertation can be summarized as follows:
(1) For a class of LPV plants which depend affinely on the vector of time-varying parameters, a robust H∞fault estimator design method is proposed. First, the robust fault estimation problem is transformed into a robust H∞control problem. Then, based on gain-scheduled techniques and bounded real lemma for LPV systems, necessary and sufficient conditions for the existence of LPV fault estimators are presented in terms of linear matrix inequalities (LMIs). When these conditions hold, a method for constructing the self-scheduled LPV estimators is proposed, and the resulting LPV estimators have the same parameter dependence as the plants. Finally, the effectiveness of the proposed method is demonstrated through an example.
(2) For a class of LPV time-delay plants where the state-space matrices depend affinely on time-varying parameters that can be measured in real-time and the time-delay is unknown but with bounded variation rates, a method for designing the robust fault estimator is presented. Based on H∞control theory and bounded real lemma for LPV time-delay systems, sufficient conditions for the existence of the self-scheduled LPV fault estimators are developed in terms of LMIs that can be solved via efficient interior-point algorithms. As these conditions hold, a robust fault estimator which has the same parameter dependence as the plant is constructed, the resulting estimator can follow the fault swiftly and effectively and with robustness to time-delay and exogenous disturbance.
(3) An integrated methd for designing robust controller and fault estimator for a class of uncertain LPV plants which depend affinely on a vector of time-varying parameters is proposed. First, the integrated design problem is reduced to an H∞control problem by introducing the concept of fault estimation error, the resulting robust control problem can be further transformed into a robust stability problem by inserting some fictitious performance blocks. Then, based on scaled H∞theory, a scaled bounded real lemma for LPV systems is proposed. On the basis of this lemma, sufficient conditions for the existence of an LPV integrated“controller/fault estimator”are developed. As these conditions hold, an algorithm for constructing the LPV integrated“controller/fault estimator”is presented. The resulting LPV“controller/fault estimator”has the same parameter dependence as the plants, and can generate both control signals and fault estimations. Finally, to demonstrate the effectiveness of the proposed method, an uncertain system with actuator faults is investigated.
(4) Based on the gain-scheduled H∞design strategy, a novel active fault-tolerant control scheme is proposed. An LPV model for systems with all possible sensor, component and actuator faults is presented. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine parameter dependence, a reconfigurable robust LPV controller is developed based on gain-scheduled H∞theory. The resulting controller is a function of the fault effect factors which can be estimated on-line from the residual vector of the fault detection and isolation (FDI) mechanism. To demonstrate the effectiveness of the proposed method, an active fault tolerant controller is designed for a double inverted pendulum system with a fault in the motor tachometer loop.
引文
[1] Challenges to control: a collective view, Report of the Workshop held at the university of Santa Clara on September 18-19,1986, IEEE Transactions on Automatic Control, 1987, 32(4): 275-285.
[2] Niederlinski A, A heuristic approach to the design of interacting multivariable systems, Automatica, 1971, 7: 691-701.
[3] Willsky A S, A survey of design methods for failure detection in dynamic systems, Automatica, 1976, 12(6): 601-611.
[4] Basseville M, Detection of abrupt changes in signal and dynamic systems, Berlin, Springer-Verlag, 1985.
[5] Basseville M, and Benveniste A, Detection of abrupt changes in signals and dynamical systems, Lecture Notes in Control and Information Sciences, vol. 77, London, Springer, 1986.
[6] Basseville M, and Nikiforov I V, Detection of abrupt changes: theory and application, New York, Prentice Hall, 1993.
[7] Gertler J, Fault detection and diagnosis in engineering systems, New York, Marcel Dekker, 1998.
[8] Patton R J, Frank P M, and Clark R N, Fault diagnosis in dynamic systems: theory and application, London, Prentice Hall, 1989.
[9] Chen J, and Patton R J, Robust model-based fault diagnosis for dynamic systems, Boston, Kluwer Academic Press, 1999.
[10] Patton R J, Frank P M and Clark R N, Issues of fault diagnosis for dynamic systems, London, Springer, 2000.
[11] Simani S, Fantuzzi C, and Patton R J, Model-based fault diagnosis in dynamic systems using identification techniques, Berlin, Springer, 2002.
[12] Frank P M, Fault diagnosis in dynamic systems using analytical and knowledge-basedredundancy– A survey and some new results, Automatica, 1990, 26(3): 459-474.
[13] Frank P M, and Ding X, Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis, Automatica, 1994, 30(4): 789-804.
[14] Frank P M, and Ding X, Survey of robust residual generation and evaluation methods in observer-based fault detection systems, Journal of Process Control, 1997, 7(6): 403-424.
[15] Isermann R, Process fault diagnosis based on modeling and estimation methods—A survey, Automatica, 1984, 20(4):387-404.
[16] Isermann R, Fault diagnosis of machines via parameter estimation and knowledge processing-tutorial paper, Automatica, 1993, 29(4): 815-835.
[17] Frank P M, On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey, International Journal of Systems Science, 1994, 25(12): 2129-2154.
[18] Garcia E A, and Frank P M, Deterministic nonlinear observer-based approaches to fault diagnosis: a survey, Control Engineering Practice, 1997, 5(5): 663-670.
[19]周东华,孙优贤,控制系统的故障检测与诊断技术,北京,清华大学出版社,1994。
[20]周东华,叶银忠,现代故障诊断与容错控制,北京,清华大学出版社,2000。
[21]闻新,张洪钺等,控制系统的故障诊断和容错控制,北京,机械工业出版社,2000。
[22]胡昌华,许化龙,控制系统故障诊断与容错控制的分析和设计,北京,国防工业出版社,2000。
[23]王仲生,智能故障诊断与容错控制,西安,西北工业大学出版社,2005。
[24] Isermann R, and Balle P, Trends in the application of model-based fault detection and diagnosis of technical processes, Control Engineering Practice, 1997, 5(5): 709-719.
[25] Van Schrick D, Remarks on terminology in the field of supervision, fault detection and diagnosis, Proc. of the IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS’97), Hull, UK, 1997, 959-964.
[26]周东华,王桂增,故障诊断技术综述,化工自动化及仪表,1998, 25(1): 58-62.
[27]陈玉东,非线性系统的故障辨识与容错控制研究,上海交通大学博士学位论文,2002。
[28] Favre C, Fly-by-wire for commercial aircraft: the Airbus experience, Int. J. Control, 1994, 59(1): 139-157.
[29] Mehra R K, Peschon J, An innovation approach to fault detection and diagnosis in dynamics, Automatica, 1971, 7: 637-640.
[30] Himmelblau D M, Fault detection and diagnosis in chemical and petrochemical process, Amsterdam, Elsevier Press, 1978.
[31] Gertler J J, Survey of model based failure detection and isolation in complex plants, IEEE Control Systems Magazine, 1988, 8(6): 3-11.
[32] Zhang X J, Auxiliary signal design in fault detection and diagnosis, Berlin, Springer-Verlag, 1991.
[33]叶银忠,潘日芳,蒋慰孙,动态系统的故障检测与诊断方法,信息与控制,1985, 15(6): 27-34.
[34]张育林,李东旭,动态系统故障诊断理论与应用,长沙,国防科技大学出版社,1997.
[35] Patton R J, and Chen J, Review of parity space approaches to fault diagnosis for aerospace systems, Journal of Guidance, Control and Dynamics, 1994, 17(2): 278-285.
[36] Patton R J, Robustness in model-based fault diagnosis: 1995 situation, Annual Reviews in Control, 1997, 21:103-123.
[37] Chow E Y, and Willsky A S, Analytic redundancy and the design of robust fault detection systems, IEEE Transaction on Automatic Control, 1984, 29(7): 603-614.
[38] Willsky A S, and Jones H L, A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems, IEEE Transactions on Automatic Control, 1976, 21: 108-121.
[39] Clark R N, Fosth D C, and Walton V M, Detecting instrument malfunctions in control systems, IEEE Trans. Aero. &. Electron. Syst., 1975, AES-11: 465-473.
[40] Clark R N, Instrument fault detection, IEEE Trans. Aero. & Electron. Syst., 1978, AES-14: 456-465.
[41] Clark R N, A simplified instrument failure detection scheme, IEEE Trans. Aero. & Electron. Syst., 1978, AES-14: 558-563.
[42] Frank P M, Fault diagnosis in dynamic system via state estimation-a survey, in Tzafestas, Singh and Schmidt (eds), System Fault Diagnostics, Reliability & Related Knowledge-basedApproaches, D. Reidel Press, Dordrecht, 1987, 1: 35-98.
[43] Bolivar A R, Garcia G, Szigeti F, and Bernussou J, A fault detection and isolation filter for linear systems with perturbations, Proceedings of the IFAC 14th world congress, Beijing, P. R. China, 1999, 563-568.
[44] Tarantino R, Szigeti F, and Colina-Morles E, Generalized Luenberger observer-based fault detection filter design: an industrial application, Control Engineering Practice, 2000, 8: 665-671.
[45] Duan G R, and Patton R. J, Robust fault detection in linear systems using Luenberger observers, Proc. of Int. Conf. on CONTROL’98, Swansea, UK, 1998, 1468-1473.
[46] Theilliol D, Mahfouf M, Sauter D, and Gama M A, Actuator fault detection, isolation method and state estimator design for hot rolling mill monitoring, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 895-900.
[47] Duan G R, Howe D, and Patton R J, Robust fault detection in descriptor systems via generalized unknown input observers, Proceedings of the IFAC 14th world congress, Beijing, P. R. China, 1999, 43-48.
[48] Chen J, Patton R. J, and Zhang H, Design of unknown input observers and robust detection filters, International Journal of Control, 1996, 63: 85-105.
[49] Odgaard P F, Lin B, and J?rgensen S B, Observer-based and regression model-based detection of emerging faults in coal mills, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 733-738.
[50] Witczak M, Korbicz J, and Puig V, An LMI approach to designing observers and unknown input observers for nonlinear systems, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 223-228.
[51] Jiang C H, and Zhou D H, Residual generator design for linear neutral delay systems, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 757-762.
[52] Edwards C, Spurgeon S K, and Patton R J, Sliding mode observers for fault detection andisolation, Automatica, 2000, 36: 541-553.
[53] Yan X G, and Edwards C, Decentralised sliding mode observer-based FDI, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 739-744.
[54] Edwards C, and Thein M, Sliding mode fault detection and isolation in a satellite leader/follower system, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 367-372.
[55] Amann P, Peronne J M, Gissinger G L, and Frank P M, Fuzzy observer for fault detection in complex systems applied to detection of critical driving situations, Proceedings of 3rd Joint COSY Workshop, Budapest, 1997, 153-158.
[56] El-ghatwary M G, Ding S X, and Gao Z W, Robust fuzzy fault detection for continuous-time nonlinear dynamic systems, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 271-276.
[57] Persis C D, A necessary condition and a backstepping observer for nonlinear fault detection, Proceedings of the IFAC 14th world congress, Beijing, P. R. China, 1999, 2896-2901.
[58] Wang H, and Daley S, Actuator fault diagnosis: An adaptive observer-based technique, IEEE Transaction on Automatic Control, 1996, 41(7): 1073-1078.
[59] Combastel C, and Zhang Q, Robust fault diagnosis based on adaptive estimation and set-membership computations, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 1273-1278.
[60] Medvedev A, Fault detection and isolation by a continuous parity space method, Automatica, 1995, 31(7): 1039-1044.
[61] Gertler J, and Monajemy R, Generating directional residuals with dynamic parity relations, Automatica, 1995, 31(4): 627-635.
[62] Gertler J, and Kunwer M K, Optimal residual decoupling for robust fault diagnosis, International Journal of Control, 1995, 61(2): 395-421.
[63] Schneider S; Weinhold N; Ding S X, and Rehm A, Parity space based FDI-scheme for vehicle lateral dynamics, Proceedings of the 2005 IEEE Conference on Control Applications,Toronto, Canada, 2005, 1409-1414.
[64] Nguang S K, Zhang P, and Ding S X, Parity relation based fault estimation for nonlinear systems: an LMI approach, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 397-402.
[65] Djeziri M A; Aitouche A; and Ould Bouamama B, Sensor fault detection of energetic system using modified parity space approach, Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, LA, USA, 2007, 2578-2583.
[66] Isermann R, and Freyermuth B, Process fault diagnosis based on process model knowledge-Part I: Principles for fault diagnosis with parameter estimation, J. Dyn. Sys., Meas. & Contr., 1991, 113(4): 620-626.
[67] Isermann R, and Freyermuth B, Process fault diagnosis based on process model knowledge-Part II: Case-study experiments, J. Dyn. Sys., Meas. & Contr., 1991, 113(4): 627-633.
[68] Isermann R, Fault diagnosis of machine via parameter estimation and knowledge processing-tutorial paper, Preprints of IFAC/IMACS sympo., SAFEPROCESS’91, Baden-Baden, 1991, 1: 121-133.
[69] Isermann R, Supervision, fault-detection and fault-diagnosis methods-an introduction, Control Engineering Practice, 1997, 5(5): 639-652.
[70]周东华,孙优贤,席裕庚,张钟俊,一类非线性系统参数偏差型故障的实时检测与诊断,自动化学报,1993, 19(2): 184-189.
[71] Magni J F, and Mouyon P, On the residual generation by observer and parity space approaches to fault detection, IEEE Transaction on Automatic Control, 1994, 39(2): 441-447.
[72] Garcia E A, and Frank P M, On the relationship between observer and parameter identification based approaches to fault detection, Proceedings of IFAC world congress, San Francisco, USA, 1996, 25-29.
[73] Gertler J, Diagnosing parametric faults: from parameter estimation to parity relation, America Control Conference, Seattle, USA, 1995, 1615-1620.
[74] Ding S X, Ding E L, and Jeinsch T, An approach to analysis and design of observer and parity relation based FDI systems, Proceedings of the IFAC 14th world congress, Beijing, P. R. China, 1999, 37-42.
[75] Frank P M, and Keller L, Sensitivity discriminating observer design for instrument failure detection, IEEE Trans. Aero. & Electron. Syst., 1981, AES-16: 460-467.
[76] Frank P M, Enhancement of robustness in observer-based fault detection, Preprints of IFAC/IMACS Symposium SAFEPROCESS’91, Baden-Baden, 1991, 1: 275-287.
[77] Patton R J, and Chen J, Observer-based fault detection and isolation: robustness and applications, Control Engineering Practice, 1997, 5(5): 671-682.
[78] Watanabe K, and Himmelblau D M, Instrument fault detection in systems with uncertainties, Int. J. Sys. Sci., 1982, 13(2); 137-158.
[79] Wünnenberg J, and Frank P M, Model-based residual generation for dynamic systems with unknown inputs, Proceedings of 12th IMACS World Congress on Scientific Computation, Paris, France, 1988, 2: 435-437.
[80] Chen J, and Zhang H Y, Robust detection of faulty actuators via unknown input observers, Int J. Sys. Sci., 1991, 22(10): 1829-1839.
[81] Hou M, Müller P C, Disturbance decoupled observer design: A unified viewpoint, IEEE Transactions on Automatic Control, 1994, 39(6): 1338-1341.
[82] Chen J, Patton R J, Optimal filtering and robust fault-diagnosis of stochastic-systems with unknown disturbances, IEE Proceedings-D: Contr. Theory and Application, 1996, 143(1): 31-36.
[83] Patton R J, Willcox S W, and Winter S J, A parameter insensitive technique for aircraft sensor fault analysis, J. of Guidance, Control and Dynamics, 1987, 10(3): 359-367.
[84] Patton R J, and Chen J, Robust fault detection using eigenstructure assignment: A tutorial consideration and some new results, Proc. of the 30th IEEE Conf. on Decision and Control, Brighton, UK, 1991, 2242-2247.
[85] Lou X, Willsky A S, and Verghese G C, Optimally robust redundancy relations for failure detection in uncertain systems, Automatica, 1986, 22(3): 333-344.
[86] Gertler J, and Singer D, A new structural framework for parity equation-based failure detection and isolation, Automatica, 1990, 26(2): 381-388.
[87] Ding X, and Guo L, An approach to time domain optimization of observer-based fault detection systems, Int. J. Control, 1998, 69(3): 419-442.
[88] Ding X, Guo L, and Jeinsch T, A characterization of parity space and its application to robust fault detection, IEEE Transactions on Automatic Control, 1999, 44(2): 337-343.
[89] Edelmayer A, Bokor J, and Keviczky L, An H∞filtering approach to robust detection of failures in dynamic systems, Proc. of the 33rd IEEE Conf. on Decision and Control, Lake Buena Vista, USA, 1994, 3037-3039.
[90] Edelmayer A, Bokor J, and Keviczky L, A scaled L2 optimisation approach for improving sensitivity of H∞detection filters for LTV systems, Preprints of the 2nd IFAC Symposium on Robust Control Design: RECOND97, Budapest, Hungary, 1997, 543-548.
[91] Niemann H, and Stoustrup J, Filter design for failure detection and isolation in the presence of modeling errors and disturbances, Proc. of the 35th IEEE Conf. on Decision and Control, Kobe, Japan, 1996, 1155-1160.
[92] Niemann H, and Stoustrup J, Integration of control and fault detection: nominal and robust design, Proc. of the IFAC Symposium on SAFEPROCESS’97, Hull, UK, 1997, 331-336.
[93] Collins E, and Song T, Robust H∞estimation and fault detection of uncertain dynamic systems. Journal of Guidance, Control and Dynamics, 2000, 23(5): 857–864.
[94] Weng Zhengxin and Shi Songjiao, Robust fault detection and isolation for nonlinear systems, Proceedings of the 3rd Asian Control Conference, Shanghai, 2000, 1099-1102.
[95] Zhong M, Ding S X, Lam J, and Wang H, An LMI approach to design robust fault detection filters for uncertain LTI systems, Automatica, 2003, 39: 543–550.
[96] Henry D, and Zolghadri A, Norm-based design of robust FDI schemes for uncertain systems under feedback control: Comparison of two approaches, Control Engineering Practice, 2006, 14(9): 1081-1097.
[97] Ma W, Sznaier M, and Lagoa C, A risk adjusted approach to robust simultaneous fault detection and isolation, Automatica, 2007, 43(3): 499-504.
[98]王红茹,王常虹,高会军,时滞离散马尔可夫跳跃系统的鲁棒故障检测,控制与决策, 2006, 21(7): 796-800.
[99] Emami-Naeini A E, Akhter M M, and Rock S M, Effect of model uncertainty on failure detection: the threshold selector, IEEE Trans. Automat. Contr., 1988, 33(12): 1106-1115.
[100] Schneider H, and Frank P M, Observer-based supervision and fault detection in robots using nonlinear and fuzzy-logic residual evaluation, IEEE Trans. Contr. Sys. Techno., 1996, 4(3): 274-282.
[101] Stoustrup J, Niemann H, and Cour-Harbo A, Optimal threshold functions for fault detection and isolation. Proceedings of 2003 American Control Conference, 2003, 1782-1787.
[102] Johansson A, Bask M, and Norlander T, Dynamic threshold generators for robust fault detection in linear systems with parameter uncertainty, Automatica, 2006, 42(7): 1095-1106.
[103] Puig V, Stancu A, Escobet T, Nejjari F, Quevedo J, and Patton R J, Passive robust fault detection using interval observers: Application to the DAMADICS benchmark problem, Control Engineering Practice, 2006, 14(6): 621-633.
[104] Hamelin F, and Sauter D, Robust fault detection in uncertain dynamic systems, Automatica, 2000, 36(11): 1747-1754.
[105] Jiang B, Wang J, and Soh Y, An adaptive technique for robust diagnosis of faults with independent effects on system outputs, International Journal of Control, 2002, 75(11): 792-802.
[106] Henry D, Zolghadri A, Monsion M, and Ygorra S, Off-line robust fault diagnosis using the generalized structured singular value, Automatica, 2002, 38(8): 1347-1358.
[107] Gu Da-Wei, and Poon F W, A robust fault-detection approach with application in a rolling-mill process, IEEE Transactions on Control Systems Technology, 2003, 11(3): 408- 414.
[108] Besancon G, High-gain observation with disturbance attenuation and application to robust fault detection, Automatica, 2003, 39(6): 1095-1102.
[109] Petersen I R, and McFarlane D C, A methodology for robust fault detection in dynamic systems, Control Engineering Practice, 2004, 12(2): 123-138.
[110] Henry D, and Zolghadri A, Design and analysis of robust residual generators for systems under feedback control, Automatica, 2005, 41(2): 251-264.
[111] Casavola A, Famularo D, and Franze G, A robust deconvolution scheme for fault detection and isolation of uncertain linear systems: an LMI approach, Automatica, 2005, 41(8): 1463-1472.
[112] Witczak M, Korbicz J, Mrugalski M, and Patton Ron J, A GMDH neural network-based approach to robust fault diagnosis: Application to the DAMADICS benchmark problem, Control Engineering Practice, 2006, 14(6): 671-683.
[113] Uppal F J, Patton R J, and Witczak M, A neuro-fuzzy multiple-model observer approach to robust fault diagnosis based on the DAMADICS benchmark problem, Control Engineering Practice, 2006, 14(6): 699-717.
[114] Viswanadham N, Taylor H J, and Luce E C, A frequency-domain approach to failure detection and isolation with application to GE-21 turbine engine control systems, Control - Theory and Advanced Technology, 1987, 3(1): 45-72.
[115] Stoustrup J, and Niemann H, Fault estimation– a standard problem approach, International Journal of Robust and Nonlinear Control, 2002, 12(8): 649-673.
[116] Stoustrup J, and Niemann H, Fault detection for nonlinear systems– a standard problem approach, Proc. of the 37th IEEE Conf. on Decision and Control, Tampa, Florida, USA, 1998, 1: 96-101.
[117] Edelmayer A, and Bokor J, Optimal H∞scaling for sensitivity optimization of detection filters, International Journal of Robust and Nonlinear Control, 2002, 12(8): 749-760.
[118] Wang H, Huang Z H, and Daley S, On the use of adaptive updating rules for actuator and sensor fault diagnosis, Automatica, 1997, 33(2): 217-225.
[119] Jiang B, Staroswiecki M, and Cocquempot V, Fault identification for a class of time-delay systems. Proceedings of the American Control Conference, Anchorage, AK, 2002, 2239-2244.
[120]王占山,张化光,一类非线性系统的鲁棒故障估计,控制与决策, 2005, 20(12): 1423-1425.
[121] Polycarpou M M, and Helmicki A J, Automated fault detection and accommodation: a learning systems approach, IEEE Transactions on Systems, Man and Cybernetics, 1995, 25(11): 1447-1458.
[122] Polycarpou M M, and Vemuri A T, Learning methodology for failure detection and accommodation, IEEE Control Systems Magazine, 1995, 15(3):16-24.
[123] Polycarpou M M, and Alexander B T, Learning approach to nonlinear fault diagnosis: detectability analysis, IEEE Transactions on Automatic Control, 2000, 45(4): 806-812.
[124] Trunov A B, and Polycarpou M M, Automated fault diagnosis in nonlinear multivariable systems using a learning methodology, IEEE Transactions on Neural Networks, 2000, 11(1):91-101.
[125] Polycarpou M M, Fault accommodation of a class of multivariable nonlinear dynamical systems using a learning approach, IEEE Transactions on Automatic Control, 2001, 46(5): 736-742.
[126] Edwards C, Spurgeon S K, and Patton R J, Sliding mode observer for fault detection and isolation, Automatica, 2000, 36(4): 541-553.
[127] Tan C P, and Edwards C, Sliding mode observers for detection and reconstruction of sensor faults, Automatica, 2002, 38(10): 1815-1821.
[128] Tan C P, and Edwards C, Sliding mode observer for robust detection and reconstruction of actuator and sensor faults, International Journal of Robust and Nonlinear Control, 2003, 13(1): 443-463.
[129] Jiang B., Cocquempot V, and Staroswiecki M, Fault estimation in nonlinear uncertain systems using robust sliding-mode observers, IEE Proceedings-D: Control Theory and Applications, 2004, 151(1): 29-37.
[130] Alwi H, and Edwards C, Robust sensor fault estimation for tolerant control of a civil aircraft using sliding modes, Proceedings of the 2006 American Control Conference, Minneapolis, Minnesota, USA, 2006, 5704-5709.
[131] Niemann H, Saberi A, Stoorvogel A A, and Sannuti P, Exact, almost and delayed fault detection– an observer based approach, Proceedings of the 1999 American ControlConference, San Diego, California, 1999, 1: 99-103.
[132] Saberi A, Stoorvogel A A, and Sannuti P, Inverse filtering and deconvolution, Proceedings of the 1999 American Control Conference, San Diego, California, 1999, 5: 3270-3274.
[133] Stoorvogel A A, Niemann H, Saberi A, and Sannuti P, Optimal fault signal estimation, International Journal of Robust and Nonlinear Control, 2002, 12(8): 697-727.
[134] Siljak D D, Reliable control using multiple control systems, Int. J. Control, 1980, 31: 303-329.
[135]叶银忠,潘日芳,蒋慰孙,多变量稳定容错控制器的设计问题,第一届过程控制科学论文报告会论文集,1987,203-209.
[136]叶银忠,潘日芳,蒋慰孙,控制系统的容错技术的回顾与展望,第二届过程控制科学论文报告会论文集,1988,49-61.
[137]葛建华,孙优贤,容错控制系统的分析与综合,杭州,浙江大学出版社,1994.
[138] Maki M, and Hagino K, Robust control with adaptation mechanism for improving transient behavior, Int. J. Control, 1999, 72: 1218-1226.
[139] Tao G, Joshi S M, and Ma X L, Adaptive state feedback and tracking control of systems with actuator failures, IEEE Trans. Autom. Contr., 2001, 46(1): 78-95.
[140] Zhang Y M, and Jiang J, Integrated active fault-tolerant control using IMM approach, IEEE Trans. Aerosp. Electron. Syst., 2001, 37(4): 1221-1235.
[141] Kim K S, Lee K J, and Kim Y D, Reconfigurable flight control system design using direct adaptive method, J. Guid. Contr., Dyn., 2003, 26(4): 543-550.
[142] Weng Z, Patton R J, and Cui P, Active fault-tolerant control of a double inverted pendulum, IMechE Part I: J. Systems and Control Engineering, 2007, 221(6): 895-904.
[143] Lin C M, and Chen C H, Robust fault-tolerant control for a biped robot using a recurrent cerebellar model articulation controller, IEEE Transactions on Systems, Man and Cybernetics, Part B, 2007, 37(1): 110-123.
[144] Wang Y, Zhou D, and Gao F, Robust fault-tolerant control of a class of non-minimum phase nonlinear processes, Journal of Process Control, 2007, 17(6): 523–537.
[145] Veillette R J, Reliable linear-quadratic state-feedback control, Automatica, 1995, 31(1):137-143.
[146] Zhao Q, and Jiang J, Reliable state feedback control system design against actuator failures, Automatica, 1998, 34(10): 1267-1272.
[147] Yang G H, Wang J L, and Soh Y C, Reliable H∞controller design for linear systems, Automatica, 2001, 37(5): 717-725.
[148] Liao F, Wang J L, and Yang G H, Reliable robust flight tracking control: An LMI approach, IEEE Trans. Contr. Syst. Technol., 2002, 10(1): 76-89.
[149] Niemann H, and Stoustrup J, Passive fault -tolerant control of a double inverted pendulum-a case study, Control Engineering Practice, 2005, 13: 1047-1059.
[150] Patton R J, Fault-tolerant control: the 1997 situation, Proc. IFAC Symposium SAFEPROCESS’97, Hull, UK, 1997, 2, 1033-1055.
[151] Packard A, Gain scheduling via linear fractional transformations, Syst. Control Lett., 1994, 22: 79-92.
[152] Apkarian P, and Gahinet P, A convex characterization of gain-scheduled H∞controllers, IEEE Trans. Autom. Control, 1995, 40(5): 853-864.
[153] Apkarian P, Gahinet P, and Becker G, Self-scheduled H∞control of linear parameter-varying systems: a design example, Automatica, 1995, 31(9): 1251-1261.
[154] Becker G, Packard A, Philbrick D, and Balas G, Control of parametrically-dependent linear systems; a single quadratic Lyapunov approach, Proc. Amer. Contr. Conf., San Francisco, CA, 1993, 2795-2799.
[155] Wu F, Yang X, Packard A, and Becker G, Induced L2-norm control for LPV system with bounded parameter variations rates, Proc. Amer. Contr. Conf., Seattle, WA, 1995, 2379-2383.
[156] Apkarian P, and Adams R J, Advanced gain-scheduling techniques for uncertain systems, IEEE Transactions on Control Systems Technology, 1998, 6(1): 21-32.
[157] Wu F, and Grigoriadis K M, LPV systems with parameter-varying time delays: analysis and control, Automatica, 2001, 37: 221-229.
[158] Mahmoud M S, New results on linear parameter-varying time-delay systems, Journal of theFranklin Institute, 2004, 341: 675-703.
[159] Bokor J, and Balas G., Detection filter design for LPV systems– a geometric approach, Automatica, 2004, 40: 511-518.
[160] Henry D, and Zolghadri A, Robust fault diagnosis in uncertain linear parameter-varying systems, 2004 IEEE International Conference on Systems, Man and Cybernetics, 2004, 6: 5165- 5170.
[161] Casavola A, Famularo D, Franze G, and Sorbara M, A fault detection filter design method for linear parameter-varying systems, MechE Part I: J. Systems and Control Engineering, 2007, 21(6): 865-874.
[162] Cui Ping, and Weng Zhengxin, Robust fault estimator design for linear parameter-varying systems, Journal of Signal Processing, 2008, 12(1): 89-95.
[163]王红茹,王常虹,高会军,一类时滞LPV系统的鲁棒故障检测,控制与决策, 2006, 21(10): 1148-1152.
[164] Zolghadri A, Henry D, and Grenaille S, Fault Diagnosis for LPV Systems, 16th Mediterranean Conference on Control and Automation, Ajaccio, France, 2008.
[165] Zhou K, and Doyle J C, Essentials of robust control, New Jersey, Prentice Hall, 1997.
[166] Gahinet P, and Apkarian P, A linear matrix inequality approach to H∞control, Int. J. Robust & Nonlinear Control, 1994, 4: 421-448.
[167] Mohammadpour J, and Grigoriadis K M, Delay-dependent H∞filtering for a class of time-delay LPV systems, Proc of the 2006 American Control Conference, Minnesota, USA, 2006, 1523-1528.
[168] Sun M, Jia Y, Du J, and Yuan S, Delay-dependent H∞control for LPV systems with time delays, Proc of the 46th IEEE Conference on Decision and Control, New Orleans, USA, 2007, 2089-2093.
[169] Zhang P, Ding S X, Wang G Z, and Zhou D H, Fault detection for multirate sampled-data systems with time delay, Int. J. Control, 2002, 75(18): 457-1471.
[170] Zhong M, Ye H, Ding S X, Wang G, and Zhou D, Fault detection filter for linear time-delay systems, Nonlinear Dynamics and Systems Theory, 2005, 5(3): 273-284.
[171] Jacobson C A, and Nett C N, An integrated approach to controls and diagnostics using the four parameter controller, IEEE Control Systems, 1991,11: 22-29.
[172] Stoustrup J, Grimble M J, and Niemann H, Design of integrated systems for the control and detection of actuator/sensor faults, Sensor Review, 1997, 17: 138-149.
[173] Marcos A, and Balas G J, A robust integrated controller/diagnosis aircraft application, International Journal of Robust and Nonlinear Control, 2005, 15: 531-551.
[174] Castro H, Bennani S, and Marcos A, Integrated vs decoupled fault detection filter & flight control law designs for a re-entry vehicle, Proc. of the 2006 IEEE international Conference on Control Applications, Munich, Germany, 2006, 3295-3300.
[175] Yamada Y, Hara S, and Fujioka H, Global optimization for constantly scaled H∞control problem, Proc. of the American Control Conference, Seattle, Washington, 1995, 427-430.
[176] Chen J, Patton R J, and Chen Z, Active fault-tolerant flight control systems design using the linear matrix inequality method, Trans. Inst. Meas. & Control, 1999; 21(2/3):77-84.
[177] Rauch H, Autonomous control reconfiguration, IEEE Con, Sys. Mag, 1995, 15:37-48.
[178] Blanke M, Frei C, Kraus F, Patton R J, and Staroswiecki M, What is fault-tolerant control? Proc. IFAC Symposium SAFEPROCESS 2000, Budapest, Hungary, 2000, 40-51.
[179] Blanke J, Kinnaert M, Lunze J, and Staroswiecki M, Diagnosis and Fault-Tolerant Control, Springer, 2003.
[180] Chen J, and Patton R J, Fault-tolerant control systems design using the LMI method, Proc. European Control Conference, Porto, Portugal, 2001, 1993-1998.
[181] Niemann H, and Poulsen J K, Analysis and design of controllers for a double inverted pendulum, Proc. American Control Conference, Denver, 2003, 2803-2808.
[2] Niederlinski A, A heuristic approach to the design of interacting multivariable systems, Automatica, 1971, 7: 691-701.
[3] Willsky A S, A survey of design methods for failure detection in dynamic systems, Automatica, 1976, 12(6): 601-611.
[4] Basseville M, Detection of abrupt changes in signal and dynamic systems, Berlin, Springer-Verlag, 1985.
[5] Basseville M, and Benveniste A, Detection of abrupt changes in signals and dynamical systems, Lecture Notes in Control and Information Sciences, vol. 77, London, Springer, 1986.
[6] Basseville M, and Nikiforov I V, Detection of abrupt changes: theory and application, New York, Prentice Hall, 1993.
[7] Gertler J, Fault detection and diagnosis in engineering systems, New York, Marcel Dekker, 1998.
[8] Patton R J, Frank P M, and Clark R N, Fault diagnosis in dynamic systems: theory and application, London, Prentice Hall, 1989.
[9] Chen J, and Patton R J, Robust model-based fault diagnosis for dynamic systems, Boston, Kluwer Academic Press, 1999.
[10] Patton R J, Frank P M and Clark R N, Issues of fault diagnosis for dynamic systems, London, Springer, 2000.
[11] Simani S, Fantuzzi C, and Patton R J, Model-based fault diagnosis in dynamic systems using identification techniques, Berlin, Springer, 2002.
[12] Frank P M, Fault diagnosis in dynamic systems using analytical and knowledge-basedredundancy– A survey and some new results, Automatica, 1990, 26(3): 459-474.
[13] Frank P M, and Ding X, Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis, Automatica, 1994, 30(4): 789-804.
[14] Frank P M, and Ding X, Survey of robust residual generation and evaluation methods in observer-based fault detection systems, Journal of Process Control, 1997, 7(6): 403-424.
[15] Isermann R, Process fault diagnosis based on modeling and estimation methods—A survey, Automatica, 1984, 20(4):387-404.
[16] Isermann R, Fault diagnosis of machines via parameter estimation and knowledge processing-tutorial paper, Automatica, 1993, 29(4): 815-835.
[17] Frank P M, On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey, International Journal of Systems Science, 1994, 25(12): 2129-2154.
[18] Garcia E A, and Frank P M, Deterministic nonlinear observer-based approaches to fault diagnosis: a survey, Control Engineering Practice, 1997, 5(5): 663-670.
[19]周东华,孙优贤,控制系统的故障检测与诊断技术,北京,清华大学出版社,1994。
[20]周东华,叶银忠,现代故障诊断与容错控制,北京,清华大学出版社,2000。
[21]闻新,张洪钺等,控制系统的故障诊断和容错控制,北京,机械工业出版社,2000。
[22]胡昌华,许化龙,控制系统故障诊断与容错控制的分析和设计,北京,国防工业出版社,2000。
[23]王仲生,智能故障诊断与容错控制,西安,西北工业大学出版社,2005。
[24] Isermann R, and Balle P, Trends in the application of model-based fault detection and diagnosis of technical processes, Control Engineering Practice, 1997, 5(5): 709-719.
[25] Van Schrick D, Remarks on terminology in the field of supervision, fault detection and diagnosis, Proc. of the IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS’97), Hull, UK, 1997, 959-964.
[26]周东华,王桂增,故障诊断技术综述,化工自动化及仪表,1998, 25(1): 58-62.
[27]陈玉东,非线性系统的故障辨识与容错控制研究,上海交通大学博士学位论文,2002。
[28] Favre C, Fly-by-wire for commercial aircraft: the Airbus experience, Int. J. Control, 1994, 59(1): 139-157.
[29] Mehra R K, Peschon J, An innovation approach to fault detection and diagnosis in dynamics, Automatica, 1971, 7: 637-640.
[30] Himmelblau D M, Fault detection and diagnosis in chemical and petrochemical process, Amsterdam, Elsevier Press, 1978.
[31] Gertler J J, Survey of model based failure detection and isolation in complex plants, IEEE Control Systems Magazine, 1988, 8(6): 3-11.
[32] Zhang X J, Auxiliary signal design in fault detection and diagnosis, Berlin, Springer-Verlag, 1991.
[33]叶银忠,潘日芳,蒋慰孙,动态系统的故障检测与诊断方法,信息与控制,1985, 15(6): 27-34.
[34]张育林,李东旭,动态系统故障诊断理论与应用,长沙,国防科技大学出版社,1997.
[35] Patton R J, and Chen J, Review of parity space approaches to fault diagnosis for aerospace systems, Journal of Guidance, Control and Dynamics, 1994, 17(2): 278-285.
[36] Patton R J, Robustness in model-based fault diagnosis: 1995 situation, Annual Reviews in Control, 1997, 21:103-123.
[37] Chow E Y, and Willsky A S, Analytic redundancy and the design of robust fault detection systems, IEEE Transaction on Automatic Control, 1984, 29(7): 603-614.
[38] Willsky A S, and Jones H L, A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems, IEEE Transactions on Automatic Control, 1976, 21: 108-121.
[39] Clark R N, Fosth D C, and Walton V M, Detecting instrument malfunctions in control systems, IEEE Trans. Aero. &. Electron. Syst., 1975, AES-11: 465-473.
[40] Clark R N, Instrument fault detection, IEEE Trans. Aero. & Electron. Syst., 1978, AES-14: 456-465.
[41] Clark R N, A simplified instrument failure detection scheme, IEEE Trans. Aero. & Electron. Syst., 1978, AES-14: 558-563.
[42] Frank P M, Fault diagnosis in dynamic system via state estimation-a survey, in Tzafestas, Singh and Schmidt (eds), System Fault Diagnostics, Reliability & Related Knowledge-basedApproaches, D. Reidel Press, Dordrecht, 1987, 1: 35-98.
[43] Bolivar A R, Garcia G, Szigeti F, and Bernussou J, A fault detection and isolation filter for linear systems with perturbations, Proceedings of the IFAC 14th world congress, Beijing, P. R. China, 1999, 563-568.
[44] Tarantino R, Szigeti F, and Colina-Morles E, Generalized Luenberger observer-based fault detection filter design: an industrial application, Control Engineering Practice, 2000, 8: 665-671.
[45] Duan G R, and Patton R. J, Robust fault detection in linear systems using Luenberger observers, Proc. of Int. Conf. on CONTROL’98, Swansea, UK, 1998, 1468-1473.
[46] Theilliol D, Mahfouf M, Sauter D, and Gama M A, Actuator fault detection, isolation method and state estimator design for hot rolling mill monitoring, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 895-900.
[47] Duan G R, Howe D, and Patton R J, Robust fault detection in descriptor systems via generalized unknown input observers, Proceedings of the IFAC 14th world congress, Beijing, P. R. China, 1999, 43-48.
[48] Chen J, Patton R. J, and Zhang H, Design of unknown input observers and robust detection filters, International Journal of Control, 1996, 63: 85-105.
[49] Odgaard P F, Lin B, and J?rgensen S B, Observer-based and regression model-based detection of emerging faults in coal mills, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 733-738.
[50] Witczak M, Korbicz J, and Puig V, An LMI approach to designing observers and unknown input observers for nonlinear systems, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 223-228.
[51] Jiang C H, and Zhou D H, Residual generator design for linear neutral delay systems, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 757-762.
[52] Edwards C, Spurgeon S K, and Patton R J, Sliding mode observers for fault detection andisolation, Automatica, 2000, 36: 541-553.
[53] Yan X G, and Edwards C, Decentralised sliding mode observer-based FDI, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 739-744.
[54] Edwards C, and Thein M, Sliding mode fault detection and isolation in a satellite leader/follower system, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 367-372.
[55] Amann P, Peronne J M, Gissinger G L, and Frank P M, Fuzzy observer for fault detection in complex systems applied to detection of critical driving situations, Proceedings of 3rd Joint COSY Workshop, Budapest, 1997, 153-158.
[56] El-ghatwary M G, Ding S X, and Gao Z W, Robust fuzzy fault detection for continuous-time nonlinear dynamic systems, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 271-276.
[57] Persis C D, A necessary condition and a backstepping observer for nonlinear fault detection, Proceedings of the IFAC 14th world congress, Beijing, P. R. China, 1999, 2896-2901.
[58] Wang H, and Daley S, Actuator fault diagnosis: An adaptive observer-based technique, IEEE Transaction on Automatic Control, 1996, 41(7): 1073-1078.
[59] Combastel C, and Zhang Q, Robust fault diagnosis based on adaptive estimation and set-membership computations, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 1273-1278.
[60] Medvedev A, Fault detection and isolation by a continuous parity space method, Automatica, 1995, 31(7): 1039-1044.
[61] Gertler J, and Monajemy R, Generating directional residuals with dynamic parity relations, Automatica, 1995, 31(4): 627-635.
[62] Gertler J, and Kunwer M K, Optimal residual decoupling for robust fault diagnosis, International Journal of Control, 1995, 61(2): 395-421.
[63] Schneider S; Weinhold N; Ding S X, and Rehm A, Parity space based FDI-scheme for vehicle lateral dynamics, Proceedings of the 2005 IEEE Conference on Control Applications,Toronto, Canada, 2005, 1409-1414.
[64] Nguang S K, Zhang P, and Ding S X, Parity relation based fault estimation for nonlinear systems: an LMI approach, Preprints of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Process, Beijing, China, 2006, 397-402.
[65] Djeziri M A; Aitouche A; and Ould Bouamama B, Sensor fault detection of energetic system using modified parity space approach, Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, LA, USA, 2007, 2578-2583.
[66] Isermann R, and Freyermuth B, Process fault diagnosis based on process model knowledge-Part I: Principles for fault diagnosis with parameter estimation, J. Dyn. Sys., Meas. & Contr., 1991, 113(4): 620-626.
[67] Isermann R, and Freyermuth B, Process fault diagnosis based on process model knowledge-Part II: Case-study experiments, J. Dyn. Sys., Meas. & Contr., 1991, 113(4): 627-633.
[68] Isermann R, Fault diagnosis of machine via parameter estimation and knowledge processing-tutorial paper, Preprints of IFAC/IMACS sympo., SAFEPROCESS’91, Baden-Baden, 1991, 1: 121-133.
[69] Isermann R, Supervision, fault-detection and fault-diagnosis methods-an introduction, Control Engineering Practice, 1997, 5(5): 639-652.
[70]周东华,孙优贤,席裕庚,张钟俊,一类非线性系统参数偏差型故障的实时检测与诊断,自动化学报,1993, 19(2): 184-189.
[71] Magni J F, and Mouyon P, On the residual generation by observer and parity space approaches to fault detection, IEEE Transaction on Automatic Control, 1994, 39(2): 441-447.
[72] Garcia E A, and Frank P M, On the relationship between observer and parameter identification based approaches to fault detection, Proceedings of IFAC world congress, San Francisco, USA, 1996, 25-29.
[73] Gertler J, Diagnosing parametric faults: from parameter estimation to parity relation, America Control Conference, Seattle, USA, 1995, 1615-1620.
[74] Ding S X, Ding E L, and Jeinsch T, An approach to analysis and design of observer and parity relation based FDI systems, Proceedings of the IFAC 14th world congress, Beijing, P. R. China, 1999, 37-42.
[75] Frank P M, and Keller L, Sensitivity discriminating observer design for instrument failure detection, IEEE Trans. Aero. & Electron. Syst., 1981, AES-16: 460-467.
[76] Frank P M, Enhancement of robustness in observer-based fault detection, Preprints of IFAC/IMACS Symposium SAFEPROCESS’91, Baden-Baden, 1991, 1: 275-287.
[77] Patton R J, and Chen J, Observer-based fault detection and isolation: robustness and applications, Control Engineering Practice, 1997, 5(5): 671-682.
[78] Watanabe K, and Himmelblau D M, Instrument fault detection in systems with uncertainties, Int. J. Sys. Sci., 1982, 13(2); 137-158.
[79] Wünnenberg J, and Frank P M, Model-based residual generation for dynamic systems with unknown inputs, Proceedings of 12th IMACS World Congress on Scientific Computation, Paris, France, 1988, 2: 435-437.
[80] Chen J, and Zhang H Y, Robust detection of faulty actuators via unknown input observers, Int J. Sys. Sci., 1991, 22(10): 1829-1839.
[81] Hou M, Müller P C, Disturbance decoupled observer design: A unified viewpoint, IEEE Transactions on Automatic Control, 1994, 39(6): 1338-1341.
[82] Chen J, Patton R J, Optimal filtering and robust fault-diagnosis of stochastic-systems with unknown disturbances, IEE Proceedings-D: Contr. Theory and Application, 1996, 143(1): 31-36.
[83] Patton R J, Willcox S W, and Winter S J, A parameter insensitive technique for aircraft sensor fault analysis, J. of Guidance, Control and Dynamics, 1987, 10(3): 359-367.
[84] Patton R J, and Chen J, Robust fault detection using eigenstructure assignment: A tutorial consideration and some new results, Proc. of the 30th IEEE Conf. on Decision and Control, Brighton, UK, 1991, 2242-2247.
[85] Lou X, Willsky A S, and Verghese G C, Optimally robust redundancy relations for failure detection in uncertain systems, Automatica, 1986, 22(3): 333-344.
[86] Gertler J, and Singer D, A new structural framework for parity equation-based failure detection and isolation, Automatica, 1990, 26(2): 381-388.
[87] Ding X, and Guo L, An approach to time domain optimization of observer-based fault detection systems, Int. J. Control, 1998, 69(3): 419-442.
[88] Ding X, Guo L, and Jeinsch T, A characterization of parity space and its application to robust fault detection, IEEE Transactions on Automatic Control, 1999, 44(2): 337-343.
[89] Edelmayer A, Bokor J, and Keviczky L, An H∞filtering approach to robust detection of failures in dynamic systems, Proc. of the 33rd IEEE Conf. on Decision and Control, Lake Buena Vista, USA, 1994, 3037-3039.
[90] Edelmayer A, Bokor J, and Keviczky L, A scaled L2 optimisation approach for improving sensitivity of H∞detection filters for LTV systems, Preprints of the 2nd IFAC Symposium on Robust Control Design: RECOND97, Budapest, Hungary, 1997, 543-548.
[91] Niemann H, and Stoustrup J, Filter design for failure detection and isolation in the presence of modeling errors and disturbances, Proc. of the 35th IEEE Conf. on Decision and Control, Kobe, Japan, 1996, 1155-1160.
[92] Niemann H, and Stoustrup J, Integration of control and fault detection: nominal and robust design, Proc. of the IFAC Symposium on SAFEPROCESS’97, Hull, UK, 1997, 331-336.
[93] Collins E, and Song T, Robust H∞estimation and fault detection of uncertain dynamic systems. Journal of Guidance, Control and Dynamics, 2000, 23(5): 857–864.
[94] Weng Zhengxin and Shi Songjiao, Robust fault detection and isolation for nonlinear systems, Proceedings of the 3rd Asian Control Conference, Shanghai, 2000, 1099-1102.
[95] Zhong M, Ding S X, Lam J, and Wang H, An LMI approach to design robust fault detection filters for uncertain LTI systems, Automatica, 2003, 39: 543–550.
[96] Henry D, and Zolghadri A, Norm-based design of robust FDI schemes for uncertain systems under feedback control: Comparison of two approaches, Control Engineering Practice, 2006, 14(9): 1081-1097.
[97] Ma W, Sznaier M, and Lagoa C, A risk adjusted approach to robust simultaneous fault detection and isolation, Automatica, 2007, 43(3): 499-504.
[98]王红茹,王常虹,高会军,时滞离散马尔可夫跳跃系统的鲁棒故障检测,控制与决策, 2006, 21(7): 796-800.
[99] Emami-Naeini A E, Akhter M M, and Rock S M, Effect of model uncertainty on failure detection: the threshold selector, IEEE Trans. Automat. Contr., 1988, 33(12): 1106-1115.
[100] Schneider H, and Frank P M, Observer-based supervision and fault detection in robots using nonlinear and fuzzy-logic residual evaluation, IEEE Trans. Contr. Sys. Techno., 1996, 4(3): 274-282.
[101] Stoustrup J, Niemann H, and Cour-Harbo A, Optimal threshold functions for fault detection and isolation. Proceedings of 2003 American Control Conference, 2003, 1782-1787.
[102] Johansson A, Bask M, and Norlander T, Dynamic threshold generators for robust fault detection in linear systems with parameter uncertainty, Automatica, 2006, 42(7): 1095-1106.
[103] Puig V, Stancu A, Escobet T, Nejjari F, Quevedo J, and Patton R J, Passive robust fault detection using interval observers: Application to the DAMADICS benchmark problem, Control Engineering Practice, 2006, 14(6): 621-633.
[104] Hamelin F, and Sauter D, Robust fault detection in uncertain dynamic systems, Automatica, 2000, 36(11): 1747-1754.
[105] Jiang B, Wang J, and Soh Y, An adaptive technique for robust diagnosis of faults with independent effects on system outputs, International Journal of Control, 2002, 75(11): 792-802.
[106] Henry D, Zolghadri A, Monsion M, and Ygorra S, Off-line robust fault diagnosis using the generalized structured singular value, Automatica, 2002, 38(8): 1347-1358.
[107] Gu Da-Wei, and Poon F W, A robust fault-detection approach with application in a rolling-mill process, IEEE Transactions on Control Systems Technology, 2003, 11(3): 408- 414.
[108] Besancon G, High-gain observation with disturbance attenuation and application to robust fault detection, Automatica, 2003, 39(6): 1095-1102.
[109] Petersen I R, and McFarlane D C, A methodology for robust fault detection in dynamic systems, Control Engineering Practice, 2004, 12(2): 123-138.
[110] Henry D, and Zolghadri A, Design and analysis of robust residual generators for systems under feedback control, Automatica, 2005, 41(2): 251-264.
[111] Casavola A, Famularo D, and Franze G, A robust deconvolution scheme for fault detection and isolation of uncertain linear systems: an LMI approach, Automatica, 2005, 41(8): 1463-1472.
[112] Witczak M, Korbicz J, Mrugalski M, and Patton Ron J, A GMDH neural network-based approach to robust fault diagnosis: Application to the DAMADICS benchmark problem, Control Engineering Practice, 2006, 14(6): 671-683.
[113] Uppal F J, Patton R J, and Witczak M, A neuro-fuzzy multiple-model observer approach to robust fault diagnosis based on the DAMADICS benchmark problem, Control Engineering Practice, 2006, 14(6): 699-717.
[114] Viswanadham N, Taylor H J, and Luce E C, A frequency-domain approach to failure detection and isolation with application to GE-21 turbine engine control systems, Control - Theory and Advanced Technology, 1987, 3(1): 45-72.
[115] Stoustrup J, and Niemann H, Fault estimation– a standard problem approach, International Journal of Robust and Nonlinear Control, 2002, 12(8): 649-673.
[116] Stoustrup J, and Niemann H, Fault detection for nonlinear systems– a standard problem approach, Proc. of the 37th IEEE Conf. on Decision and Control, Tampa, Florida, USA, 1998, 1: 96-101.
[117] Edelmayer A, and Bokor J, Optimal H∞scaling for sensitivity optimization of detection filters, International Journal of Robust and Nonlinear Control, 2002, 12(8): 749-760.
[118] Wang H, Huang Z H, and Daley S, On the use of adaptive updating rules for actuator and sensor fault diagnosis, Automatica, 1997, 33(2): 217-225.
[119] Jiang B, Staroswiecki M, and Cocquempot V, Fault identification for a class of time-delay systems. Proceedings of the American Control Conference, Anchorage, AK, 2002, 2239-2244.
[120]王占山,张化光,一类非线性系统的鲁棒故障估计,控制与决策, 2005, 20(12): 1423-1425.
[121] Polycarpou M M, and Helmicki A J, Automated fault detection and accommodation: a learning systems approach, IEEE Transactions on Systems, Man and Cybernetics, 1995, 25(11): 1447-1458.
[122] Polycarpou M M, and Vemuri A T, Learning methodology for failure detection and accommodation, IEEE Control Systems Magazine, 1995, 15(3):16-24.
[123] Polycarpou M M, and Alexander B T, Learning approach to nonlinear fault diagnosis: detectability analysis, IEEE Transactions on Automatic Control, 2000, 45(4): 806-812.
[124] Trunov A B, and Polycarpou M M, Automated fault diagnosis in nonlinear multivariable systems using a learning methodology, IEEE Transactions on Neural Networks, 2000, 11(1):91-101.
[125] Polycarpou M M, Fault accommodation of a class of multivariable nonlinear dynamical systems using a learning approach, IEEE Transactions on Automatic Control, 2001, 46(5): 736-742.
[126] Edwards C, Spurgeon S K, and Patton R J, Sliding mode observer for fault detection and isolation, Automatica, 2000, 36(4): 541-553.
[127] Tan C P, and Edwards C, Sliding mode observers for detection and reconstruction of sensor faults, Automatica, 2002, 38(10): 1815-1821.
[128] Tan C P, and Edwards C, Sliding mode observer for robust detection and reconstruction of actuator and sensor faults, International Journal of Robust and Nonlinear Control, 2003, 13(1): 443-463.
[129] Jiang B., Cocquempot V, and Staroswiecki M, Fault estimation in nonlinear uncertain systems using robust sliding-mode observers, IEE Proceedings-D: Control Theory and Applications, 2004, 151(1): 29-37.
[130] Alwi H, and Edwards C, Robust sensor fault estimation for tolerant control of a civil aircraft using sliding modes, Proceedings of the 2006 American Control Conference, Minneapolis, Minnesota, USA, 2006, 5704-5709.
[131] Niemann H, Saberi A, Stoorvogel A A, and Sannuti P, Exact, almost and delayed fault detection– an observer based approach, Proceedings of the 1999 American ControlConference, San Diego, California, 1999, 1: 99-103.
[132] Saberi A, Stoorvogel A A, and Sannuti P, Inverse filtering and deconvolution, Proceedings of the 1999 American Control Conference, San Diego, California, 1999, 5: 3270-3274.
[133] Stoorvogel A A, Niemann H, Saberi A, and Sannuti P, Optimal fault signal estimation, International Journal of Robust and Nonlinear Control, 2002, 12(8): 697-727.
[134] Siljak D D, Reliable control using multiple control systems, Int. J. Control, 1980, 31: 303-329.
[135]叶银忠,潘日芳,蒋慰孙,多变量稳定容错控制器的设计问题,第一届过程控制科学论文报告会论文集,1987,203-209.
[136]叶银忠,潘日芳,蒋慰孙,控制系统的容错技术的回顾与展望,第二届过程控制科学论文报告会论文集,1988,49-61.
[137]葛建华,孙优贤,容错控制系统的分析与综合,杭州,浙江大学出版社,1994.
[138] Maki M, and Hagino K, Robust control with adaptation mechanism for improving transient behavior, Int. J. Control, 1999, 72: 1218-1226.
[139] Tao G, Joshi S M, and Ma X L, Adaptive state feedback and tracking control of systems with actuator failures, IEEE Trans. Autom. Contr., 2001, 46(1): 78-95.
[140] Zhang Y M, and Jiang J, Integrated active fault-tolerant control using IMM approach, IEEE Trans. Aerosp. Electron. Syst., 2001, 37(4): 1221-1235.
[141] Kim K S, Lee K J, and Kim Y D, Reconfigurable flight control system design using direct adaptive method, J. Guid. Contr., Dyn., 2003, 26(4): 543-550.
[142] Weng Z, Patton R J, and Cui P, Active fault-tolerant control of a double inverted pendulum, IMechE Part I: J. Systems and Control Engineering, 2007, 221(6): 895-904.
[143] Lin C M, and Chen C H, Robust fault-tolerant control for a biped robot using a recurrent cerebellar model articulation controller, IEEE Transactions on Systems, Man and Cybernetics, Part B, 2007, 37(1): 110-123.
[144] Wang Y, Zhou D, and Gao F, Robust fault-tolerant control of a class of non-minimum phase nonlinear processes, Journal of Process Control, 2007, 17(6): 523–537.
[145] Veillette R J, Reliable linear-quadratic state-feedback control, Automatica, 1995, 31(1):137-143.
[146] Zhao Q, and Jiang J, Reliable state feedback control system design against actuator failures, Automatica, 1998, 34(10): 1267-1272.
[147] Yang G H, Wang J L, and Soh Y C, Reliable H∞controller design for linear systems, Automatica, 2001, 37(5): 717-725.
[148] Liao F, Wang J L, and Yang G H, Reliable robust flight tracking control: An LMI approach, IEEE Trans. Contr. Syst. Technol., 2002, 10(1): 76-89.
[149] Niemann H, and Stoustrup J, Passive fault -tolerant control of a double inverted pendulum-a case study, Control Engineering Practice, 2005, 13: 1047-1059.
[150] Patton R J, Fault-tolerant control: the 1997 situation, Proc. IFAC Symposium SAFEPROCESS’97, Hull, UK, 1997, 2, 1033-1055.
[151] Packard A, Gain scheduling via linear fractional transformations, Syst. Control Lett., 1994, 22: 79-92.
[152] Apkarian P, and Gahinet P, A convex characterization of gain-scheduled H∞controllers, IEEE Trans. Autom. Control, 1995, 40(5): 853-864.
[153] Apkarian P, Gahinet P, and Becker G, Self-scheduled H∞control of linear parameter-varying systems: a design example, Automatica, 1995, 31(9): 1251-1261.
[154] Becker G, Packard A, Philbrick D, and Balas G, Control of parametrically-dependent linear systems; a single quadratic Lyapunov approach, Proc. Amer. Contr. Conf., San Francisco, CA, 1993, 2795-2799.
[155] Wu F, Yang X, Packard A, and Becker G, Induced L2-norm control for LPV system with bounded parameter variations rates, Proc. Amer. Contr. Conf., Seattle, WA, 1995, 2379-2383.
[156] Apkarian P, and Adams R J, Advanced gain-scheduling techniques for uncertain systems, IEEE Transactions on Control Systems Technology, 1998, 6(1): 21-32.
[157] Wu F, and Grigoriadis K M, LPV systems with parameter-varying time delays: analysis and control, Automatica, 2001, 37: 221-229.
[158] Mahmoud M S, New results on linear parameter-varying time-delay systems, Journal of theFranklin Institute, 2004, 341: 675-703.
[159] Bokor J, and Balas G., Detection filter design for LPV systems– a geometric approach, Automatica, 2004, 40: 511-518.
[160] Henry D, and Zolghadri A, Robust fault diagnosis in uncertain linear parameter-varying systems, 2004 IEEE International Conference on Systems, Man and Cybernetics, 2004, 6: 5165- 5170.
[161] Casavola A, Famularo D, Franze G, and Sorbara M, A fault detection filter design method for linear parameter-varying systems, MechE Part I: J. Systems and Control Engineering, 2007, 21(6): 865-874.
[162] Cui Ping, and Weng Zhengxin, Robust fault estimator design for linear parameter-varying systems, Journal of Signal Processing, 2008, 12(1): 89-95.
[163]王红茹,王常虹,高会军,一类时滞LPV系统的鲁棒故障检测,控制与决策, 2006, 21(10): 1148-1152.
[164] Zolghadri A, Henry D, and Grenaille S, Fault Diagnosis for LPV Systems, 16th Mediterranean Conference on Control and Automation, Ajaccio, France, 2008.
[165] Zhou K, and Doyle J C, Essentials of robust control, New Jersey, Prentice Hall, 1997.
[166] Gahinet P, and Apkarian P, A linear matrix inequality approach to H∞control, Int. J. Robust & Nonlinear Control, 1994, 4: 421-448.
[167] Mohammadpour J, and Grigoriadis K M, Delay-dependent H∞filtering for a class of time-delay LPV systems, Proc of the 2006 American Control Conference, Minnesota, USA, 2006, 1523-1528.
[168] Sun M, Jia Y, Du J, and Yuan S, Delay-dependent H∞control for LPV systems with time delays, Proc of the 46th IEEE Conference on Decision and Control, New Orleans, USA, 2007, 2089-2093.
[169] Zhang P, Ding S X, Wang G Z, and Zhou D H, Fault detection for multirate sampled-data systems with time delay, Int. J. Control, 2002, 75(18): 457-1471.
[170] Zhong M, Ye H, Ding S X, Wang G, and Zhou D, Fault detection filter for linear time-delay systems, Nonlinear Dynamics and Systems Theory, 2005, 5(3): 273-284.
[171] Jacobson C A, and Nett C N, An integrated approach to controls and diagnostics using the four parameter controller, IEEE Control Systems, 1991,11: 22-29.
[172] Stoustrup J, Grimble M J, and Niemann H, Design of integrated systems for the control and detection of actuator/sensor faults, Sensor Review, 1997, 17: 138-149.
[173] Marcos A, and Balas G J, A robust integrated controller/diagnosis aircraft application, International Journal of Robust and Nonlinear Control, 2005, 15: 531-551.
[174] Castro H, Bennani S, and Marcos A, Integrated vs decoupled fault detection filter & flight control law designs for a re-entry vehicle, Proc. of the 2006 IEEE international Conference on Control Applications, Munich, Germany, 2006, 3295-3300.
[175] Yamada Y, Hara S, and Fujioka H, Global optimization for constantly scaled H∞control problem, Proc. of the American Control Conference, Seattle, Washington, 1995, 427-430.
[176] Chen J, Patton R J, and Chen Z, Active fault-tolerant flight control systems design using the linear matrix inequality method, Trans. Inst. Meas. & Control, 1999; 21(2/3):77-84.
[177] Rauch H, Autonomous control reconfiguration, IEEE Con, Sys. Mag, 1995, 15:37-48.
[178] Blanke M, Frei C, Kraus F, Patton R J, and Staroswiecki M, What is fault-tolerant control? Proc. IFAC Symposium SAFEPROCESS 2000, Budapest, Hungary, 2000, 40-51.
[179] Blanke J, Kinnaert M, Lunze J, and Staroswiecki M, Diagnosis and Fault-Tolerant Control, Springer, 2003.
[180] Chen J, and Patton R J, Fault-tolerant control systems design using the LMI method, Proc. European Control Conference, Porto, Portugal, 2001, 1993-1998.
[181] Niemann H, and Poulsen J K, Analysis and design of controllers for a double inverted pendulum, Proc. American Control Conference, Denver, 2003, 2803-2808.