Simultaneous actuator and sensor fault estimation for neutral-type systems via intermediate observer

2021 ◽  
pp. 014233122110585
Author(s):  
Yuheng Wei ◽  
Dongbing Tong ◽  
Qiaoyu Chen ◽  
Yuqing Sun ◽  
Wuneng Zhou
Keyword(s):  
Neutral Type ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Type Systems ◽  
Matching Condition ◽  
Descriptor System ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Sensor Faults ◽  
Neutral Type Systems

This study addresses the fault estimation (FE) issue for neutral-type systems with sensor faults and actuator faults through the intermediate observer. First, it is well-known that the observer matching condition (OMC) ought to be met for most traditional FE methods, which is actually difficult to satisfy for many systems. In order to overcome this limitation, a suitable variable is designed and the intermediate observer is proposed to estimate the actuator and sensor faults for neutral-type systems simultaneously. Second, based on linear matrix inequalities, sufficient conditions are derived, which guarantee the existence of the intermediate observer. An augmented descriptor system is constructed for the neutral-type systems. By the Lyapunov stability theory, states of error systems are ultimately bounded. Finally, two examples demonstrate the effectiveness and practicability of the designed strategy.

scholarly journals Robust Fault Estimation and Fault-Tolerant Control Based on Sliding Mode Observer for Takagi–Sugeno Fuzzy Systems Subject to Actuator and Sensor Faults

2020 ◽  
Vol 9 (8) ◽  
pp. 145-154
Keyword(s):  
Fault Tolerant ◽  
Sliding Mode ◽  
Sufficient Conditions ◽  
Fault Tolerant Control ◽  
Lyapunov Stability Theory ◽  
Sliding Mode Observer ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Sensor Faults ◽  
Takagi Sugeno

In this paper, the problems of fault estimation and fault-tolerant control for Takagi-Sugeno fuzzy system affected by simultaneous actuator faults, sensor faults and external disturbances are investigated. Firstly, an adaptive fuzzy sliding-mode observer is designed to simultaneously estimate system states and both actuator and sensor faults. Then, based on the online estimation information, a static output feedback fault-tolerant controller is designed to compensate for the effect of faults and to stabilize the closed-loop system. Moreover, sufficient conditions for the existence of the proposed observer and controller with an H∞ performance are derived based on Lyapunov stability theory and expressed in terms of linear matrix inequalities. Finally, a nonlinear inverted pendulum with cart system application is given illustrate the validity of the proposed method.

scholarly journals Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: Application to an anaerobic bioreactor

2015 ◽  
Vol 25 (2) ◽  
pp. 233-244 ◽  
Author(s):  
Francisco-Ronay López-Estrada ◽  
Jean-Christophe Ponsart ◽  
Carlos-Manuel Astorga-Zaragoza ◽  
Jorge-Luis Camas-Anzueto ◽  
Didier Theilliol
Keyword(s):  
Sufficient Conditions ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Anaerobic Bioreactor ◽  
Lpv Systems ◽  
Sensor Fault Detection ◽  
System States ◽  
Sensor Fault Estimation

Abstract This paper addresses the design of a state estimation and sensor fault detection, isolation and fault estimation observer for descriptor-linear parameter varying (D-LPV) systems. In contrast to where the scheduling functions depend on some measurable time varying state, the proposed method considers the scheduling function depending on an unmeasurable state vector. In order to isolate, detect and estimate sensor faults, an augmented system is constructed by considering faults to be auxiliary state vectors. An unknown input LPV observer is designed to estimate simultaneously system states and faults. Sufficient conditions to guarantee stability and robustness against the uncertainty provided by the unmeasurable scheduling functions and the influence of disturbances are synthesized via a linear matrix inequality (LMI) formulation by considering H∞ and Lyapunov approaches. The performances of the proposed method are illustrated through the application to an anaerobic bioreactor model.

Fault estimation and observer-based fault-tolerant controller in finite frequency domain

2017 ◽  
Vol 40 (5) ◽  
pp. 1659-1668 ◽  
Author(s):  
Yingying Tian ◽  
Fanglai Zhu
Keyword(s):  
Frequency Domain ◽  
Controller Design ◽  
Fault Tolerant ◽  
Sufficient Conditions ◽  
Original System ◽  
Descriptor System ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Finite Frequency ◽  
Closed Loop System

In this paper, the problems of finite-frequency fault estimation (FE) and fault tolerant controller design are investigated for a class of systems subjected to both sensor and actuator faults. To begin with, by introducing an expanded state vector, the original system is transformed into a descriptor system, and then an unknown input proportional-integral observer (PI) is developed to provide state and FE, which avoids the overdesign problems occurring in the entire frequency domain. After this, based on reconstructed information, an observer-based fault-tolerant controller is designed to stabilize the closed-loop system even if it suffers from faults and disturbances. In addition, the sufficient conditions of the existence of the PI and fault tolerant controller are derived by linear matrix inequality (LMI) tools. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed techniques.

Adaptive state estimation of Markov switched neural networks driven by Lévy noise

2019 ◽  
Vol 42 (2) ◽  
pp. 330-336
Author(s):  
Dongbing Tong ◽  
Qiaoyu Chen ◽  
Wuneng Zhou ◽  
Yuhua Xu
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Lévy Noise ◽  
Linear Matrix ◽  
Delayed Neural Networks ◽  
Switched Neural Networks ◽  
Inequality Technique ◽  
Levy Noise

This paper proposes the [Formula: see text]-matrix method to achieve state estimation in Markov switched neural networks with Lévy noise, and the method is very distinct from the linear matrix inequality technique. Meanwhile, in light of the Lyapunov stability theory, some sufficient conditions of the exponential stability are derived for delayed neural networks, and the adaptive update law is obtained. An example verifies the condition of state estimation and confirms the effectiveness of results.

scholarly journals Observer Design for Simultaneous State and Fault Estimation for a Class of Discrete-time Descriptor Linear Models

2021 ◽  
Vol 229 ◽  
pp. 01020
Author(s):  
Kaoutar Ouarid ◽  
Abdellatif El Assoudi ◽  
Jalal Soulami ◽  
El Hassane El Yaagoubi
Keyword(s):  
Discrete Time ◽  
Linear Models ◽  
Estimation Error ◽  
Equivalent Form ◽  
Observer Design ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Sensor Faults ◽  
Actuator Faults ◽  
Algebraic Equations

This paper investigates the problem of observer design for simultaneous states and faults estimation for a class of discrete-time descriptor linear models in presence of actuator and sensor faults. The idea of the present result is based on the second equivalent form of implicit model [1] which permits to separate the differential and algebraic equations in the considered singular model, and the use of an explicit augmented model structure. At that stage, an observer is built to estimate simultaneously the unknown states, the actuator faults, and the sensor faults. Next, the explicit structure of the augmented model is established. Then, an observer is built to estimate simultaneously the unknown states, the actuator faults, and the sensor faults. By using the Lyapunov approach, the convergence of the state estimation error of the augmented system is analyzed, and the observer’s gain matrix is achieved by solving only one linear matrix inequality (LMI). At long last, an illustrative model is given to show the performance and capability of the proposed strategy.

scholarly journals H∞ Filtering for Discrete-Time Nonlinear Singular Systems with Quantization

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yifu Feng ◽  
Zhi-Min Li ◽  
Xiao-Heng Chang
Keyword(s):  
Discrete Time ◽  
Filter Design ◽  
Design Method ◽  
Singular Systems ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Linear Matrix ◽  
Nonlinear Singular Systems ◽  
Attenuation Level ◽  
Measurement Output

This paper investigates the problem of H∞ filtering for class discrete-time Lipschitz nonlinear singular systems with measurement quantization. Assume that the system measurement output is quantized by a static, memoryless, and logarithmic quantizer before it is transmitted to the filter, while the quantizer errors can be treated as sector-bound uncertainties. The attention of this paper is focused on the design of a nonlinear quantized H∞ filter to mitigate quantization effects and ensure that the filtering error system is admissible (asymptotically stable, regular, and causal), while having a unique solution with a prescribed H∞ noise attenuation level. By introducing some slack variables and using the Lyapunov stability theory, some sufficient conditions for the existence of the nonlinear quantized H∞ filter are expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is presented to demonstrate the effectiveness of the proposed quantized filter design method.

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