scholarly journals Actuator Fault Detection for Discrete-Time Descriptor Systems via a Convex Unknown Input Observer with Unknown Scheduling Variables

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Javier Martinez ◽  
Braulio Aguiar ◽  
Víctor Estrada-Manzo ◽  
Miguel Bernal
Keyword(s):  
Fault Detection ◽  
Discrete Time ◽  
Estimation Error ◽  
Mean Value ◽  
Descriptor Systems ◽  
Mean Value Theorem ◽  
Linear Matrix ◽  
Unknown Input ◽  
Actuator Fault ◽  
And Performance

This paper presents actuator fault detection of discrete-time nonlinear descriptor systems by means of nonlinear unknown input observers. The approach is based on the exact factorization of the estimation error in order to overcome the well-known problem of unmeasurable scheduling variables within the observation of convex models, thus avoiding the use of Lipschitz constants, differential mean value theorem, or robust techniques. As a result, the designing conditions are cast in terms of linear matrix inequalities and efficiently solved via commercially available software. Numerical as well as academic setups are provided to illustrate the advantages and performance of the proposal.

scholarly journals Robust nonlinear observer design for actuator fault detection in diesel engines

2013 ◽  
Vol 23 (3) ◽  
pp. 557-569 ◽  
Author(s):  
Boulaid Boulkroune ◽  
Issam Djemili ◽  
Abdel Aitouche ◽  
Vincent Cocquempot
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Estimation Error ◽  
Nonlinear Observer ◽  
Disturbance Attenuation ◽  
Mean Value Theorem ◽  
Unknown Input ◽  
Actuator Fault ◽  
Unknown Input Observer ◽  
Varying Coefficients

Abstract This paper is concerned with actuator fault detection in nonlinear systems in the presence of disturbances. A nonlinear unknown input observer is designed and the output estimation error is used as a residual for fault detection. To deal with the problem of high Lipschitz constants, a modified mean-value theorem is used to express the nonlinear error dynamics as a convex combination of known matrices with time-varying coefficients. Moreover, the disturbance attenuation is performed using a modified H∞ criterion. A sufficient condition for the existence of an unknown input observer is obtained using a linear matrix inequality formula, and the observer gains are obtained by solving the corresponding set of inequalities. The advantages of the proposed method are that no a priori assumption on the unknown input is required and that it can be applied to a large class of nonlinear systems. Performances of the proposed approach are shown through the application to a diesel engine model.

Reduced-Order Observer Design for Discrete-Time Descriptor Systems With Unknown Inputs: An Linear Matrix Inequality Approach

2015 ◽  
Vol 137 (8) ◽  
Author(s):  
Shenghui Guo ◽  
Fanglai Zhu
Keyword(s):  
Linear Matrix Inequality ◽  
Discrete Time ◽  
Observer Design ◽  
Descriptor Systems ◽  
Linear Matrix ◽  
Unknown Input ◽  
Matrix Inequality ◽  
Reduced Order ◽  
Unknown Inputs ◽  
Reduced Order Observer

Reduced-order observer design methods for both linear and nonlinear discrete-time descriptor systems based on the linear matrix inequality (LMI) approach are investigated. We conclude that the conditions under which a full-order observer exists can also guarantee the existence of a reduced-order observer. By choosing a special reduced-order observer gain matrix, a reduced-order unknown input observer is proposed for linear system with unknown inputs, and then an unknown input reconstruction is provided for some special cases. We also extend above results to the cases of nonlinear systems. Finally, three numerical comparative simulation examples are given to illustrate the effectiveness and merits of proposed methods.

Nonlinear unknown input observer using mean value theorem and genetic algorithm

2019 ◽  
Vol 10 (02) ◽  
pp. 1950001 ◽  
Author(s):  
Ramzi Ben Messaoud
Keyword(s):  
Genetic Algorithm ◽  
Secure Communication ◽  
Estimation Error ◽  
Mean Value ◽  
Stability Study ◽  
Observer Design ◽  
Theoretical Development ◽  
Mean Value Theorem ◽  
Unknown Input ◽  
Unknown Input Observer

In this note, we consider a new unknown input observer design for nonlinear systems. The main idea consists in determining the estimation error and mean value theorem parameters ([Formula: see text]) to introduce them into proposed observer structure. This process is designed on the basis of mean value theorem and genetic algorithm. The stability study relies on the use of a classical quadratic Lyapunov function. The observer’s gains are determined systematically. For the validation of theoretical development proposed in this paper, we consider two practical realizations that deals with the secure communication problem.

scholarly journals LMI Formulation for Static Output Feedback Design of Discrete-Time Switched Systems

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
Selma Ben Attia ◽  
Salah Salhi ◽  
Mekki Ksouri
Keyword(s):  
Discrete Time ◽  
Output Feedback ◽  
Small Degree ◽  
Switched System ◽  
Linear Matrix ◽  
Static Output Feedback ◽  
Additional Degree ◽  
Feedback Design ◽  
And Performance ◽  
Static Output

This paper concerns static output feedback design of discrete-time linear switched system using switched Lyapunov functions (SLFs). A new characterization of stability for the switched system under arbitrary switching is first given together with -performance evaluation. The various conditions are given through a family of LMIs (Linear Matrix Inequalities) parameterized by a scalar variable which offers an additional degree of freedom, enabling, at the expense of a relatively small degree of complexity in the numerical treatment (one line search), to provide better results compared to previous one. The control is defined as a switched static output feedback which guarantees stability and -performance for the closed-loop system. A numerical example is presented to illustrate the effectiveness of the proposed conditions.

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 Optimal guaranteed cost filtering for Markovian jump discrete-time systems

2004 ◽  
Vol 2004 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Magdi S. Mahmoud ◽  
Peng Shi
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Estimation Error ◽  
Linear Matrix ◽  
State Estimator ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Discrete Time Systems ◽  
Linear State ◽  
Time Systems

This paper develops a result on the design of robust steady-state estimator for a class of uncertain discrete-time systems with Markovian jump parameters. This result extends the steady-state Kalman filter to the case of norm-bounded time-varying uncertainties in the state and measurement equations as well as jumping parameters. We derive a linear state estimator such that the estimation-error covariance is guaranteed to lie within a certain bound for all admissible uncertainties. The solution is given in terms of a family of linear matrix inequalities (LMIs). A numerical example is included to illustrate the theory.

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