Active fault detection based on set‐membership approach for uncertain discrete‐time systems

Active fault detection facilitates determination of the fault characteristics by injecting proper auxiliary input signals into the system. This article proposes an observer‐based on‐line active fault detection method for discrete‐time systems with bounded uncertainties. First, the output including disturbances, measurement noise and interval uncertainties at each sample time is enclosed in a zonotope. In order to reduce the conservativeness in the fault detection process, a zonotopic observer is designed to estimate the system states allowing to generate the output zonotopes. Then, a proper auxiliary input signal is designed to separate the output zonotopes of the faulty model from the healthy model that is injected into the system to facilitate the detection of small fault . Since the auxiliary input signal generation leads to a nonconvex optimization problem, it is transformed into a mixed integer quadratic programming problem. Finally, a case study based on a DC motor is used to show the effectiveness of the proposed method.     

Generalized set‐theoretic unknown input observer for LPV systems with application to state estimation and robust fault detection

This paper proposes to design an unknown input observer (UIO) for the linear‐parameter‐varying (LPV) system on the basis of the set theory, which is named as the set‐theoretic UIO (SUIO). The advantage of the SUIO consists in that it combines active and passive approaches to obtain robustness in state estimation (SE) and fault detection (FD). The active approach is based on the use of UIO to decouple unknown inputs, while the passive approach is based on the set theory to bound uncertain factors that cannot be actively decoupled. As a result, the effect of both unknown inputs (process disturbances, modeling errors, etc.) and measurement noises can be appropriately handled in the residual signals compared with the standard UIO‐based SE and FD approaches. The design of SUIO can overcome the limitations of the traditional UIO design conditions, which can significantly broaden the application of the UIO‐based SE and FD theory. Moreover, this paper proposes a generalized framework that can provide more flexibility in the design of SUIO guaranteeing their stability by means of a group of matrix inequalities. Because the LPV system uses a collection of online obtainable scheduling variables to embed nonlinearities, the design of SUIO for the LPV system can be used to address the SE and FD problems of nonlinear systems. At the end of this paper, two case studies are used to illustrate the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.     

Interval set‐membership estimation for continuous linear systems

This article proposes a mixed interval set‐membership estimation (ISME) method for continuous linear time‐invariant (LTI) systems by combining the positive system theory and the set theory. The proposed ISME method gives a new mixed interval‐set estimation framework for continuous LTI systems, whose benefit consists in that it has potential to achieve a balance of computational complexity and robust state estimation conservatism with respect to the interval observer (IO) and the set‐valued observer (SVO) for continuous LTI systems. Particularly, the proposed ISME method first uses a coordinate transformation such that the original system is transformed into an equivalent system. Second, the equivalent system is partitioned into two subsystems, where the first subsystem has a Meztler and Hurwitz subsystem matrix and then an IO is designed for the first subsystem based on the positive system theory. Because it is not guaranteed that the second subsystem also has a Meztler and Hurwitz subsystem matrix, a zonotopic SVO is further designed for the second subsystem based on the set theory. Consequently, an integration of the two steps above provides the whole SE results for the original system. At the end of this article, an example is used to illustrate the effectiveness of the proposed ISME method.     

A nonlinear set‐membership filter for on‐line applications

A guaranteed estimator for a general class of nonlinear systems and on‐line usage is developed and analysed. This filter bounds the linearization error, then applies a linear set‐membership filter such that stability guarantees hold for nonlinear systems. A tight bound on the linearization error is found using interval analysis. This filter recursively estimates an ellipsoidal set in which the true state lies. General assumptions include the use of bounded noises and twice continuously differentiable dynamics. When the system is uniformly observable, it is proven that the nonlinear set‐membership filter is stable. In addition, if no noise is present and the initial error is small, the error between the centre of the estimated set and the true value converges to zero. The result is an estimator which is computationally attractive and can be implemented robustly in real‐time. The proposed method is applied to a two‐state example to demonstrate the theoretical results. Copyright © 2003 John Wiley & Sons, Ltd.     

Design of optimal interval observers using set‐theoretic methods for robust state estimation

This article aims to design an optimal interval observer for discrete linear time‐invariant systems. Particularly, the proposed design method first transforms the interval observer into a zonotopic set‐valued observer by establishing an explicit mathematical relationship between the interval observer and the zonoptopic set‐valued observer. Then, based on the established mathematical relationship, a locally optimal observer gain is designed for the interval observer via the equivalent zonotopic set‐valued observer structure and the Frobenious norm‐based size of zonotopes. Third, considering that the dynamics of the optimal interval observer becomes a discrete linear time‐varying system due to the designed time‐varying optimal gain, an optimization problem to obtain a coordinate transformation matrix and the locally optimal observer gain for the interval observer is formulated and handled. Finally, a theoretic comparison on the conservatism of the interval observer and the zonotopic set‐valued observer is made. At the end of this article, a microbial growth bioprocess is used to illustrate the effectiveness of the proposed method.     

An iterative learning observer for fault detection and accommodation in nonlinear time‐delay systems

This article addresses fault detection, estimation, and compensation problem in a class of disturbance driven time delay nonlinear systems. The proposed approach relies on an iterative learning observer (ILO) for fault detection, estimation, and compensation. When there are no faults in the system, the ILO supplies accurate disturbance estimation to the control system where the effect of disturbances on estimation error dynamics is attenuated. At the same time, the proposed ILO can detect sudden changes in the nonlinear system due to faults. As a result upon the detection of a fault, the same ILO is used to excite an adaptive control law in order to offset the effect of faults on the system. Further, the proposed ILO‐based adaptive fault compensation strategy can handle multiple faults. The overall fault detection and compensation strategy proposed in the paper is finally demonstrated in simulation on an automotive engine example to illustrate the effectiveness of this approach. Copyright © 2005 John Wiley & Sons, Ltd.     

改进的全对称多胞形集员状态估计算法

针对带有有界的噪声和参数的非线性离散时间系统, 提出了一种改进的全对称多胞形集员状态估计算法.在算法的时间更新过程中, 采用区间算术的方法计算一个包含系统轨迹的全对称多胞形.在算法的量测更新过程中, 则要首先在状态空间中给出一个集合作为与量测输出相一致的区域的外界描述, 然后计算一个具有最小容积的全对称多胞形作为时间更新全对称多胞形与此集合的交集的外界描述.由于此集合可表示为多个带的交集, 所以需要研究全对称多胞形与带的交集的外界描述方法.在提出改进的外界描述方法之前, 指出了原始外界描述方法的保守性.改进的外界描述方法给出了新的包含二者交集的全对称多胞形族, 然后找到具有最小容积的全对称多胞形作为二者交集的外界描述. 此后证明了改进外界描述方法得到的全对称多胞形不会比原始方法大. 最后, 采用仿真实验来检验不同噪声分布对算法性能的影响.仿真结果表明了改进算法得到的状态估计的均方误差和全对称多胞形的容积比原始算法小, 而且当存在重尾分布噪声时此优势更加明显.     

A UD factorization‐based nonlinear adaptive set‐membership filter for ellipsoidal estimation

The extended set‐membership filter (ESMF) for nonlinear ellipsoidal estimation suffers from numerical instability, computation complexity as well as the difficulty in filter parameter selection. In this paper, a UD factorization‐based adaptive set‐membership filter is developed and applied to nonlinear joint estimation of both time‐varying states and parameters. As a result of using the proposed UD factorization, combined with a new sequential and selective measurement update strategy, the numerical stability and real‐time applicability of conventional ESMF are substantially improved. Furthermore, an adaptive selection scheme of the filter parameters is derived to reduce the computation complexity and achieve sub‐optimal estimation. Simulation results have shown the efficiency and robustness of the proposed method. Copyright © 2007 John Wiley & Sons, Ltd.     

An interval observer for uncertain continuous‐time linear systems

To design robust interval observers for uncertain continuous‐time linear systems, a new set‐integration approach is proposed to compute trajectory tubes for the estimation error. Because this approach, the order‐preserving condition on the dynamics of the estimation error is no longer required. Therefore, synthesis methods can be used to compute observer gains that reduce the impact of the system uncertainties on the accuracy of the estimated state enclosures. The performance of the proposed approach is showcased through illustrative numerical examples.     

H−/L∞ fault detection observer for linear parameter‐varying systems with parametric uncertainty

This paper considers H_?/L_∞ fault detection for discrete‐time linear parameter‐varying (LPV) systems with parametric uncertainty. In H_?/L_∞ fault detection scheme, residual generation and threshold computation are simultaneously designed. With consideration of H_?/L_∞ performance indices, the generated residual is sensitive to faults while robust against unknown disturbances. Furthermore, the L_∞ performance provides a time‐varying threshold for residual evaluation. This paper proposes a novel H_?/L_∞ fault detection observer design method to handle actuator fault detection for LPV systems with parametric uncertainty. Sufficient conditions of the fault detection observer design in the finite‐frequency domain are derived as linear matrix inequalities. Numerical simulations are used to illustrate the effectiveness and superiority of the proposed fault detection observer design approach.     

Interval observers for discrete‐time systems

Interval observers are constructed for discrete‐time systems. First, time‐invariant interval observers are proposed for a family of nonlinear systems. Second, it is shown that, for any time‐invariant exponentially stable discrete‐time linear system with additive disturbances, time‐varying exponentially stable discrete‐time interval observers can be constructed. The latter result relies on the design of time‐varying changes of coordinates, which transform a linear system into a nonnegative one. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimal reduced-order interval observer design for uncertain continuous-time linear systems

This article investigates the reduced-order interval observer (R-IO) design technique for continuous-time linear systems with unknown external disturbances and measurement noises. First, we propose a coupled R-IO structure with more design degrees of freedom, and it can be directly applied not only to solve the difficulty of the error system cooperativity construction but to relax the constraint on the sensor measurement noises. Second, the R-IO existence condition is derived as a set of matrix equations (MEs), and a complete solution, explicitly showing the available design parameters, to such an R-IO is further obtained by solving the MEs. Third, using the solution, an integrated optimization indicator of the R-IO performance is built as the valid selection mechanism of these parameters. Finally, the efficiency of the obtained results is illustrated through a numerical example and a practical example.     

Recursive observer‐based fault detection for a class of nonlinear uncertain systems with output constraints

This paper investigates the fault detection (FD) problem for a class of nonlinear uncertain systems in strict feedback form with an output constraint. The key idea is to design an observer to generate the FD signals and the output estimate, which also satisfies the output constraint. To facilitate constraint handling, the constraints on the output and the output estimate are transformed into the output estimation error constraint. Then, the FD observer is designed in a recursive framework. By employing a barrier Lyapunov function, the output estimation error constraint is incorporated in the last step of the recursive observer design algorithm to prevent constraint violation. It is shown that the output estimation error is uniformly bounded and satisfies the constraint for the fault‐free case. Furthermore, the residual signal is constructed by the output estimation error, and its corresponding bound is used as threshold. Compared with the FD method without considering the constraints, the proposed FD scheme provides a smaller threshold and characterizes a larger set of faults, which can be detected. Finally, simulation results are presented to illustrate the benefits of the proposed FD scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

基于自适应观测器控制系统的快速故障调节

针对基于自适应观测器故障调节设计中的两个难点,即系统满足严格正实(SPR)条件与故障估计的准确性和快速性,首先引入适当的坐标变换,有效地放松了严格SPR条件,适用于一大类控制系统的故障诊断;其次,针对变换后的系统,提出一种快速故障估计的设计方法,明显改善了故障估计的性能;再次,基于故障估计值修正控制律以补偿故障所带来的影响,使故障调节后的系统稳定;最后通过仿真实验验证了该方法的有效性.     

Robust fault detection of uncertain linear systems via quasi-LMIs

A novel H_∞ deconvolution filter design method is discussed for fault detection of uncertain polytopic linear systems subject to unknown inputs. The enhancement of fault sensitivity is characterized in terms of an H_- index. By means of the Projection Lemma and Congruence Transformations, a quasi-convex LMI formulation of the design problem is obtained. The effectiveness of the filter is illustrated via a numerical example.     

Invariant‐set‐based fault diagnosis in Lure systems

In this paper, we present an invariant‐set‐based method for actuator and sensor fault detection and isolation in Lure systems. The Lure plant is controlled by an observer‐based feedback tracking controller, designed for the nominal (fault‐free) system. Suitable residual signals are constructed from measurable system outputs and estimates associated with the nominal observer. Faults are diagnosed by online contrasting the residual signal trajectories against sets of values that the residuals are shown to attain under healthy or faulty operation. These values are obtained via set‐invariance analysis of the system closed‐loop trajectories. Copyright © 2013 John Wiley & Sons, Ltd.     

Interval observer-based fault detectability analysis using mixed set-invariance theory and sensitivity analysis approach

This paper addresses the characterisation of the minimum detectable fault (MDF) by means of residual sensitivity integrated with the set-invariance theory when using an interval observer-based approach as a fault detection (FD) scheme. Uncertainties (disturbances and noise) are considered as of unknown but bounded nature (i.e. in the set-membership framework). A zonotopic-set representation towards reducing set operations to simple matrix calculations is utilised to bound the state/output estimations provided by the interval observer-based approach. In order to show the connection between sensitivity and set-invariance analyses, mathematical expressions of the MDF are derived when considering different types of faults. Finally, a simulation case study based on a quadruple-tank system is employed to both illustrate and discuss the effectiveness of the proposed approach. The interval observer-based FD scheme is used to test the MDF obtained from the integration of both residual sensitivity analysis and set-invariance theory in the considered case study.     

基于观测器的非线性不确定系统鲁棒故障检测新方法

针对非线性不确定系统的鲁棒故障检测问题,提出了一种采用统计理论的新方法.通过设计全阶故障观测器产生残差信号,将鲁棒故障检测观测器设计问题转化为H∞优化问题;利用H∞范数描述故障检测的鲁棒性,保证系统的抗干扰能力,同时引入H_范数,确保对故障信号的灵敏度;应用线性矩阵不等式技术给出了该设计问题解存在的条件和求解方法.将统计理论用于故障检测阈值的确定,充分考虑了残差信号的随机特性,使故障决策更加准确和可靠.最后通过仿真实例验证了本文方法的有效性.