scholarly journals Fault detection in a three-tank hydraulic system using unknown input observer and extended Kalman filter

2021 ◽  
Vol 9 (4A) ◽  
Author(s):  
Ayman E. O. HASSAN ◽  
◽  
Tasnim A. A. MOHAMMED ◽  
Aşkın DEMİRKOL ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Hydraulic System ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Matrix Inequality ◽  
Sensor Faults ◽  
Unknown Input Observer ◽  
System A

This paper presents the problem of fault diagnosis in a three-tank hydraulic system. A mathematical model of the system is developed in order to apply two different observing algorithms. Unknown Input Observer (UIO) and Extended Kalman Filter (EKF) have been used to detect and isolate actuator and sensor faults. For Unknown Input Observer (UIO), residuals are calculated from the measured and estimated output according to the eigenvalues of the system after processed by Linear Matrix Inequality (LMI). Extended Kalman filter uses process and measurement noise variances for state estimation. Unknown Input Observer and Extended Kalman Filter's performance in fault estimation and isolation is evaluated under different scenarios. Using Extended Kalman Filter (EKF), faults can be diagnosed effectively in the presence of noise, while Unknown Input Observer (UIO) is working better in the absence of noise, and simulation results illustrate that clearly.

Robust Unknown Input Observer for Nonlinear Systems and Its Application to Fault Detection and Isolation

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
S. Mondal ◽  
G. Chakraborty ◽  
K. Bhattacharyya
Keyword(s):  
Fault Diagnosis ◽  
Nonlinear System ◽  
Fault Detection ◽  
Nonlinear Function ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Matrix Inequality ◽  
Unknown Input Observer ◽  
Linear Matrix Inequality Approach

A robust unknown input observer for a nonlinear system whose nonlinear function satisfies the Lipschitz condition is designed based on linear matrix inequality approach. Both noise and uncertainties are taken into account in deriving the observer. A component fault detection and isolation scheme based on these observers is proposed. The effectiveness of the observer and the fault diagnosis scheme is shown by applying them for component fault diagnosis of an electrohydraulic actuator.

scholarly journals A Nonlinear Observer to Estimate the Effective Reproduction Number of Infectious Diseases

2021 ◽  
Vol 4 (1) ◽  
pp. 39-45
Author(s):  
Agus Hasan
Keyword(s):  
Kalman Filter ◽  
Infectious Diseases ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Reproduction Number ◽  
Epidemiological Data ◽  
Sir Model ◽  
Nonlinear Observer ◽  
Linear Matrix ◽  
Matrix Inequality

In this paper, we design a Nonlinear Observer (NLO) to estimate the effective reproduction number (Rt) of infectious diseases. The NLO is designed from a discrete-time augmented Susceptible-Infectious-Removed (SIR) model. The observer gain is obtained by solving a Linear Matrix Inequality (LMI). The method is used to estimate Rt in Jakarta using epidemiological data during COVID-19 pandemic. If the observer gain is tuned properly, this approach produces similar result compared to existing approach such as Extended Kalman filter (EKF).

scholarly journals Active fault tolerance control of a wind turbine system using an unknown input observer with an actuator fault

2018 ◽  
Vol 28 (1) ◽  
pp. 69-81 ◽  
Author(s):  
Shanzhi Li ◽  
Haoping Wang ◽  
Abdel Aitouche ◽  
Yang Tian ◽  
Nicolai Christov
Keyword(s):  
Wind Turbine ◽  
Active Fault ◽  
Fault Tolerant ◽  
Lyapunov Theory ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Actuator Fault ◽  
Unknown Input Observer ◽  
Wind Turbine System

AbstractThis paper proposes a fault tolerant control scheme based on an unknown input observer for a wind turbine system subject to an actuator fault and disturbance. Firstly, an unknown input observer for state estimation and fault detection using a linear parameter varying model is developed. By solving linear matrix inequalities (LMIs) and linear matrix equalities (LMEs), the gains of the unknown input observer are obtained. The convergence of the unknown input observer is also analysed with Lyapunov theory. Secondly, using fault estimation, an active fault tolerant controller is applied to a wind turbine system. Finally, a simulation of a wind turbine benchmark with an actuator fault is tested for the proposed method. The simulation results indicate that the proposed FTC scheme is efficient.

Robust Unknown Input Observer-Based Fast Adaptive Fault Estimation

2015 ◽  
pp. 427-456 ◽  
Author(s):  
Olfa Hrizi ◽  
Boumedyen Boussaid ◽  
Ahmed Zouinkhi ◽  
M. Naceur Abdelkrim
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Estimation ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Unknown Input Observer ◽  
Free System ◽  
Robust Observer ◽  
Technological Advances ◽  
The Family

This chapter studies the problem of fault estimation using a fast adaptive fault diagnosis observer. Note that the advance of observer-based fault diagnosis is outlined and the idea of fault class estimation is introduced and studied. A new form of the estimator bloc considered for this purpose is an Unknown Input Observer (UIO). This observer is designed for an unknown input and fault free system, which is obtained by coordinate transformations of original systems with unknown inputs (disturbance) and faults. Stability of the adaptive estimation is provided by a Lyapunov function ending with solving the Linear Matrix Inequalities (LMI). Due to technological advances in the field of electronic devices, the family of robots is of particular interest. To overcome the drawback of robots' model responses when including a fault, a robust observer is adopted for a Pioneer robot to improve the fault estimation and thereafter to repair its trajectory.

scholarly journals A Nonlinear Observer to Estimate the Effective Reproduction Number of Infectious Diseases

2021 ◽  
Author(s):  
Agus Hasan
Keyword(s):  
Kalman Filter ◽  
Infectious Diseases ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Reproduction Number ◽  
Epidemiological Data ◽  
Sir Model ◽  
Nonlinear Observer ◽  
Linear Matrix ◽  
Matrix Inequality

AbstractIn this paper, we design a Nonlinear Observer (NLO) to estimate the effective reproduction number (ℛt) of infectious diseases. The NLO is designed from a discrete-time augmented Susceptible-Infectious-Removed (SIR) model. The observer gain is obtained by solving a Linear Matrix Inequality (LMI). The method is used to estimate ℛt in Jakarta using epidemiological data during COVID-19 pandemic. If the observer gain is tuned properly, this approach produces similar result compared to existing approach such as Extended Kalman filter (EKF).

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.

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.

Sign in / Sign up

Export Citation Format

Share Document