基于未知输入观测器的非线性系统故障诊断

针对一类含未知输入和执行器故障的非线性系统,提出基于未知输入观测器的故障诊断算法,改进了Luenberger故障诊断观测器对系统出现未知扰动时的不足.利用广义逆方法,将未知输入从残差信号中完全解耦,通过产生对故障高敏感性以及对未知扰动强抗扰动性的观测器实现系统的故障诊断,并通过Lyapunov函数用线性矩阵不等式保证了系统稳定性.     

一类伴有部分解耦干扰的非线性系统故障诊断

针对Lipschitz非线性系统执行器故障检测和传感器故障估计问题,本文提出了一种基于H-/L_∞未知输入观测器的有限频域故障诊断策略.首先,将系统处理成包含传感器故障的增广系统.然后,将该系统的未知输入干扰分为可解耦与不可解耦两部分.针对可解耦部分,利用观测器匹配条件将其从估计误差中消除.针对不可解耦部分,设计L_∞指标抑制其对残差的影响并结合有限频域H-指标提高执行器故障检测灵敏度.接着,给出观测器存在的充分条件并将其转化为受LMIs约束的线性优化问题,实现了执行器故障的鲁棒检测及传感器故障的鲁棒估计.最后,结合仿真算例验证了所提方法的正确性与有效性.     

基于广义未知输入观测器的鲁棒故障估计方法

当干扰存在时,有效地估计故障且放松故障的限制条件需要进一步的研究,为此针对含未知干扰的非线性连续系统的鲁棒故障估计问题提出一种广义未知输入观测器方法。首先,将执行器故障向量和传感器故障向量与原系统状态向量组成广义系统,放松对故障类型的限制,对此广义系统设计未知输入观测器解耦干扰,保证鲁棒性的同时估计出状态变量、执行器故障及其一阶微分和传感器故障。然后通过解线性矩阵不等式（LMI）给出估计误差渐近收敛的条件。最后,在MATLAB 的simulink平台上用三叶片水平轴风力模型仿真验证本文观测器的故障估计有效性鲁棒性。     

基于逆系统迭代学习观测器的故障调节方法

针对一类满足Lipschitz条件的多输入多输出非线性可逆系统执行器故障问题,提出了一种基于迭代学习观测器的逆系统内模故障调节方法。引入PD型迭代学习策略,设计了迭代学习故障诊断观测器,用于对执行器未知时变故障进行快速、准确估计。根据故障估计值,结合逆系统方法对逆模型进行补偿,使得补偿后的逆模型与非线性被控对象串联仍为伪线性系统;再结合内模控制实现了伪线性系统的容错控制。最后,通过仿真算例验证了该方案的有效性。     

基于滑模观测器和广义观测器的故障估计方法

针对受未知干扰影响的一类非线性系统,提出一种基于滑模观测器和广义观测器的执行器故障和传感器故障估计方法.首先通过线性变换将原系统解耦为两个降阶的子系统,其中一个子系统受执行器故障和干扰的影响,另一个含有传感器故障和干扰,进一步将后一个子系统转化为广义系统.对两类子系统分别设计滑模观测器和广义观测器,给出估计误差一致最终有界的条件,得到系统状态和未知干扰的估计值.然后,利用等效输出控制原理重构执行器故障,引入干扰补偿保证重构算法的鲁棒性,再根据广义观测器的结果获得传感器故障的估计值.最后,通过计算机仿真验证了本文方法的有效性.     

无未知参数先验信息的非线性自适应观测器设计

研究了一类具有未知参数的非线性系统自适应观测器设计问题．不同于现有结果,本文所研究的非线性系统更为。一般,已知的系统信息更少：1）系统未知参数的范数的上界未知;2）具有关于可测输出非Lipschitz连续的非线性动态;3）系统输出显式地依赖于控制输入．通过设计自适应调节器来估计未知参数范数,从而给出了不基于未知参数先验信息的非线性自适应观测器设计的新方法．所设计的观测器为令局渐近收敛的,即实现了系统状态的渐近重构,确保了未知参数估计的一致有界性．此外,在系统输出不显式地依赖于控制输入的条件下,研究了降维观测器的设计问题．仿真例子验证了本文理论结果的正确性．     

无未知参数先验信息的非线性自适应观测器设计

研究了一类具有未知参数的非线性系统自适应观测器设计问题.不同于现有结果,本文所研究的非线性系统更为一般,已知的系统信息更少:1)系统未知参数的范数的上界未知;2)具有关于可测输出非Lipschitz连续的非线性动态:3)系统输出显式地依赖于控制输入.通过设计自适应调节器来估计未知参数范数,从而给出了不基于未知参数先验信息的非线性自适应观测器设计的新方法.所设计的观测器为全局渐近收敛的,即实现了系统状态的渐近重构,确保了未知参数估计的一致有界性.此外,在系统输出不显式地依赖于控制输入的条件下,研究了降维观测器的设计问题.仿真例子验证了本文理论结果的正确性.     

基于自适应未知输入观测器的非线性动态系统故障诊断

针对以往故障诊断研究中要求故障或故障导数及系统干扰的上界是已知的不足,以及难以同时诊断执行器故障和传感器故障的问题,提出一种自适应未知输入故障诊断观测器,能够同时重构非线性动态系统的执行器故障和传感器故障.首先,利用H_∞性能指标抑制未知输入对故障重构的影响,采用Lyapunov泛函得到观测误差动态系统的稳定性;然后,通过线性矩阵不等式求解观测器增益阵,并实现故障重构;最后,通过直流电机系统的仿真验证了所提出方法的有效性.     

基于未知输入观测器的导弹间歇故障诊断

针对带有未知扰动和噪声的导弹间歇故障诊断问题,设计了一种基于未知输入观测器的导弹问题故障诊断方法,系统的输入部分或全部未知情况下也能获取系统状态的称为未知输入观测器.首先,为实现对外部扰动的解耦,设计降维未知输入观测器,并通过滑动时间窗口得到对间歇故障敏感而对未知扰动解耦的残差信号;然后,在满足误报率和漏报率的条件下,通过假设检验,确定了间歇故障发生时刻和消失时刻的可检测阈值;最后,对所提出的方法进行了仿真验证.仿真结果表明,在误差允许的范围内,设计的方法能够实现对间歇故障检测,满足实时性和准确性的要求.     

Lipschitz广义非线性系统观测器设计

研究一类广义非线性系统的观测器设计问题．首先讨论了半正定Lyapunov函数下指数1广义非线性系统稳定及渐近稳定性,然后对一类由线性和Lipschitz非线性项组成的广义非线性系统,给出了渐近稳定观测器存在的条件,并把观测器反馈增益矩阵的设计归结为广义线性系统容许控制以及奇异值计算问题,证明了若容许广义线性系统矩阵的最小奇异值大于系统的Lipschitz常数,容许控制器增益矩阵就是待求的观测器反馈增益矩阵。     

Design of unknown input functional observers for nonlinear systems with application to fault diagnosis

This paper considers the design of low-order unknown input functional observers for robust fault detection and isolation of a class of nonlinear Lipschitz systems subject to unknown inputs. The proposed functional observers can be used to generate residual signals to detect and isolate actuator faults. By using the generalized inverse approach, the effect of the unknown inputs can be decoupled completely from the residual signals. Conditions for the existence and stability of reduced-order unknown input functional observer are derived. A design procedure for the generation of residual signals to detect and isolate actuator faults is presented using the proposed unknown-input observer-based approach. A numerical example is given to illustrate the proposed fault diagnosis scheme in nonlinear systems subject to unknown inputs.     

Robust fault diagnosis scheme in a class of nonlinear system based on UIO and fuzzy residual

In this study, a novel robust fault diagnosis scheme is developed for a class of nonlinear systems when both fault and disturbance are considered. The proposed scheme includes both component and sensor fault with nonlinear system that transferred to nonlinear Takagi-Sugeno (T-S) model. It considers a larger category of nonlinear system when fuzzification is used for only nonlinear distribution matrices. In fact the proposed method covers nonlinear systems could not transform to linear T-S model. This paper studies the problem of robust fault diagnosis based on two fuzzy nonlinear observers, the first one is a fuzzy nonlinear unknown input observer (FNUIO) and the other is a fuzzy nonlinear Luenberger observer (FNLO). This approach decouples the faulty subsystem from the rest of the system through a series of transformations. Then, the objective is to design FNUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method; meanwhile, FNLO is designed for faulty subsystem to generate fuzzy residual signal based on a quadratic Lyapunov function and some matrices inequality convexification techniques. FNUIO affects only the fault free subsystem and completely removes any unknown inputs such as disturbances when residual signal is generated by FNLO is affected by component or sensor fault. This novelty and using nonlinear system in T-S model make the proposed method extremely effective from last decade literature. Sufficient conditions are established in order to guarantee the convergence of the state estimation error. Thus, a residual generator is determined on the basis of LMI conditions such that the estimation error is completely sensitive to fault vector and insensitive to the unknown inputs. Finally, an numerical example is given to show the highly effectiveness of the proposed fault diagnosis scheme.     

Adaptive nonlinear observer for state and unknown parameter estimation in noisy systems

This paper proposes a novel adaptive observer for Lipschitz nonlinear systems and dissipative nonlinear systems in the presence of disturbances and sensor noise. The observer is based on an H_∞ observer that can estimate both the system states and unknown parameters by minimising a cost function consisting of the sum of the square integrals of the estimation errors in the states and unknown parameters. The paper presents necessary and sufficient conditions for the existence of the observer, and the equations for determining observer gains are formulated as linear matrix inequalities (LMIs) that can be solved offline using commercially available LMI solvers. The observer design has also been extended to the case of time-varying unknown parameters. The use of the observer is demonstrated through illustrative examples and the performance is compared with extended Kalman filtering. Compared to previous results on nonlinear observers, the proposed observer is more computationally efficient, and guarantees state and parameter estimation for two very broad classes of nonlinear systems (Lipschitz and dissipative nonlinear systems) in the presence of input disturbances and sensor noise. In addition, the proposed observer does not require online computation of the observer gain.     

LMI approach to robust unknown input observer design for continuous systems with noise and uncertainties

The full order robust unknown input observers for continuous systems are presented. The observers are designed for both linear and nonlinear systems considering both noise and uncertainties. First, an unknown input observer is designed for linear systems. The observer is derived based on linear matrix inequality (LMI) approach. Then the observer design problem is extended for a class of nonlinear systems whose nonlinear function satisfies the Lipschitz condition. The main advantage of these observers over the existing works on UIO design is that these can handle both noise and uncertainties simultaneously. The performance of the observers is demonstrated by applying it to the robust state estimation of single link robot arm.     

Robust sliding-mode observer-based multiple-fault diagnosis scheme

In this study, we simultaneously evaluate the multiple-fault diagnosis problem of a class of Lipschitz nonlinear systems with actuator and sensor faults and unknown input disturbances. A nonsingular system transformation is used to transform the original system into two subsystems for multiple-fault diagnosis: subsystems 1 and 2. At the system level, two robust sliding-mode observers (RSMOs) are proposed. An RSMO is designed for subsystem 1 to detect actuator faults subjected to unknown input disturbances, and another RSMO is designed for subsystem 2 to detect sensor faults subjected to actuator faults. At the component level, a bank of RSMOs is proposed to detect and isolate actuators (sensors) with faults using a dedicated observer scheme. The reachability of RSMOs is comprehensively investigated in the estimation error space. Accordingly, the proposed observer parameters are designed as an optimization problem and solved using the linear matrix inequality (LMI) optimization technique. The effectiveness of the proposed multiple-fault diagnosis scheme was validated through simulations of a modified seventh-order aircraft system.     

具有自适应调节律的非线性系统执行器故障检测和隔离方法

针对受到外部干扰的非线性系统,讨论了基于观测器的执行器故障检测和隔离方法.首先,通过引入一个对Lipschitz非线性项Lipschitz常数自适应调节的微分调节项,使得观测器具有自适应性,从而使观测器设计具有无须知道Lipschitz常数大小的优点;然后,通过一滑模控制项来抑制干扰,使观测器具有鲁棒性,并在此基础上,结合多观测器故障隔离的思想,提出了执行器故障检测和隔离方法;最后,通过对一个七阶飞行器实际模型的仿真,表明了该方法的实用性.     

Integrated fault estimation and fault‐tolerant control for stochastic systems with Brownian motions

This paper presents an integrated robust fault estimation and fault‐tolerant control technique for stochastic systems subjected to Brownian parameter perturbations. The augmented system approach, unknown input observer method, and optimization technique are integrated to achieve robust simultaneous estimates of the system states and the means of faults concerned. Meanwhile, a robust fault‐tolerant control strategy is developed by using actuator and sensor signal compensation techniques. Stochastic linear time‐invariant systems, stochastic systems with Lipschitz nonlinear constraint, and stochastic systems with quadratic inner‐bounded nonlinear constraint are respectively investigated, and the corresponding fault‐tolerant control algorithms are addressed. Finally, the effectiveness of the proposed fault‐tolerant control techniques is demonstrated via the drivetrain system of a 4.8 MW benchmark wind turbine, a 3‐tank system, and a numerical nonlinear model.     

基于有限时间未知输入观测器的一类受扰非线性系统故障检测与估计

针对一类非线性系统同时存在执行器故障、传感器故障和扰动的问题,提出一种基于有限时间未知输入观测器的故障检测与估计方法.首先,通过线性非奇异变换将原系统解耦为两个降阶的子系统,其中一个子系统只包含扰动,另一个子系统同时包含扰动和故障;然后,通过一阶低通滤波器获得新的状态并与子系统构成增广系统,实现将原系统的传感器故障转化为增广系统的执行器故障;接着,设计未知输入观测器对增广系统故障进行检测,实现在有限时间内估计出系统的扰动和故障,并通过理论分析验证所设计观测器的有限时间收敛性;最后,基于永磁同步电机(PMSM)转速系统进行仿真研究,仿真结果验证了所提出方法的有效性.     

Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network

In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.     

Sensor fault estimation for Lipschitz nonlinear systems in finite-frequency domain

In this study, the problem of sensor fault estimation observer design for Lipschitz nonlinear systems with finite-frequency specifications is investigated. First, the sensor fault is considered as an auxiliary state vector and an augmented system is established. Then, by transforming the nonlinear error dynamics into a linear parameter varying system, a sufficient condition for the observer-error system with a finite-frequency H_∞ performance is derived in terms of linear matrix inequalities (LMIs). Based on the obtained condition, novel nonlinear observers are designed to simultaneously estimate the system states and the fault signals and attenuate the disturbances in the finite-frequency domain. The proposed design method can provide less restrictive LMI conditions and get a better disturbance-attenuation performance when the frequency ranges of disturbances are known beforehand. A numerical example is given to show the effectiveness and superiority of the new results.