Atef Khedher

Bio: Atef Khedher is an academic researcher. The author has contributed to research in topics: Observer (quantum physics) & Fault (power engineering). The author has an hindex of 7, co-authored 19 publications receiving 139 citations.

State and sensor faults estimation via a proportional integral observer

Abstract: This paper deals with the problem of fault detection and identification in noisy systems. A proportionnal integral observer with unknown inputs is used to reconstruct state and sensors faults. A mathematical transformation is made to conceive an augmented system, in which the initial sensor fault appear as an unknown input. The noise effect on the state and fault estimation errors is also minimized. The obtained results are then extended to nonlinear systems described by nonlinear Takagi-Sugeno models.

Adaptive observer for fault estimation in nonlinear systems described by a Takagi-Sugeno model

Abstract: This paper deals with the problem of fault estimation for linear and nonlinear systems. An adaptive proportional integral observer is designed to estimate both the system state and sensor and actuator faults which can affect the system. The model of the system is first augmented in such a manner that the original sensor faults appear as actuator faults in this new model. The faults are then considered as unknown inputs and are estimated using a classical proportional-integral observer. The proposed method is first developed for linear systems and is then extended to nonlinear ones that can be represented by a Takagi-Sugeno model. In the two cases, examples of low dimensions illustrate the effectiveness of the proposed method.

State and Faults Estimation Based on Proportional Integral Sliding Mode Observer for Uncertain Takagi–Sugeno Fuzzy Systems and its Application to a Turbo-Reactor

Abstract: This paper deals with the problem of state and faults estimation for nonlinear uncertain systems described by Takagi–Sugeno fuzzy structures (called also multiple models). In this work, actuator faults are considered as unknown inputs. The state and faults estimation is made using a structure of sliding mode observer where an integral term is added. This new structure of observer is called proportional integral sliding mode observer. The added integral term permits the unknown input estimation. For the sensor faults estimation, a mathematical transformation is used. The application of this mathematical transformation to the initial system output let to conceive an augmented system where the initial sensor fault appears as an unknown input. The observer convergence conditions are formulated in the form of Linear Matrix Inequalities allowing computing the observer gains. The proposed proportional integral sliding mode observer is applied to a numerical example showing the efficiency of the fault and the state estimation. In order to show the efficiency of the proposed method, it is applied to a turbo-reactor system.

State Estimation via Observers with Unknown Inputs: Application to a Particular Class of Uncertain Takagi-Sugeno Systems

Abstract: This paper deals with the design of a multiple observer allowing estimating the state vector of a nonlinear system described by a Takagi-Sugeno multiple model subject to modelling and input uncertainties which are considered as unknown inputs. The main contribution of the paper is the conception of a multiple observer based on the elimination of these unknown inputs. Convergence conditions are established in order to guarantee the convergence of the state estimation error. These conditions are expressed in Linear Matrix Inequality (LMI) formulation. An example of simulation is given to illustrate the proposed method.

An approach of faults estimation in Takagi-Sugeno fuzzy systems

Abstract: In this work, the problem of fault detection and identification in systems described by Takagi-Sugeno fuzzy systems is studied. A proportional integral observer is conceived in order to reconstruct state and faults which can affect the system. In order to estimate actuator and sensor faults, a mathematical transformation is made to conceive an augmented system, in which the initial sensor fault appears as an actuator fault. Considering actuator fault as an unknown input, one can use an unknown inputs estimation method. The noise effect on the state and fault estimation is also minimized.

Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

Abstract: The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.

A Model-Based Fault Detection and Prognostics Scheme for Takagi–Sugeno Fuzzy Systems

Abstract: In this paper, a novel model-based fault detection (FD) and prediction scheme is developed for a class of Takagi-Sugeno (T-S) fuzzy systems. Unlike other FD schemes, in the proposed design, an FD observer with online fault learning capability is utilized to generate a residual which is obtained by comparing the system output with respect to the observer output. A fault is declared active if the generated residual exceeds an a priori chosen threshold. Subsequently, the fault magnitude is estimated online by using a suitable parameter update law. Upon detection, the online estimate of the fault magnitude is used in a mathematical equation to determine time-to-failure (TTF) or remaining useful life. TTF is determined by projecting the estimated fault magnitude at the current time instant against a failure threshold. Note that the previously reported FD schemes could neither estimate the magnitude of a growing fault in real time nor were they able to predict the remaining useful life of the fuzzy system. In this paper, the stability of the proposed FD and prognostics scheme is verified using the Lyapunov theory. Finally, two different simulation case studies are considered to verify the theoretical conjectures presented in this paper.

A Novel Approach to State and Unknown Input Estimation for Takagi–Sugeno Fuzzy Models With Applications to Fault Detection

Abstract: In this paper, a novel approach is proposed for state and unknown input estimation of Takagi-Sugeno fuzzy systems. By introducing an augmented state vector, containing both system state and the unknown input, a functional observer is proposed to estimate this vector, and the proposed observer provides a highly flexible estimation output. Through casting the observer design problem into an equivalent solvability problem of a linear matrix equation with respect to the observer gains, the existence condition for the proposed observer is explicitly derived in terms of matrix rank. Furthermore, a parameterization methodology of the observer gain matrices is provided as well, which avoids directly solving the Sylvester equation. The proposed observer design scheme is further applied to the $H\_{}/H_\infty $ fault detection problem, and the effectiveness of the proposed approach is demonstrated by state and unknown input estimation for a tunnel diode circuit and fault detection for a continuously stirred tank reactor system.

Fuzzy Fault-Tolerant Control of Wind-Diesel Hybrid Systems Subject to Sensor Faults

Abstract: A fuzzy fault-tolerant control (FFTC) framework is proposed for wind-diesel-hybrid systems (WDHS) with time-varying bounded sensor faults. The algorithm utilizes fuzzy systems based on “Takagi-Sugeno” (TS) fuzzy models to represent nonlinear systems. A fuzzy proportional-integral estimation observer (FPIEO) design is proposed to achieve fault estimation of TS models with abrupt sensor faults. Sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability and are formulated in the format of linear matrix inequalities (LMIs) to obtain controller gains and observer gains. The proposed algorithm maximizes the produced power, minimizes the voltage ripple, and is able to maintain the system's stability during the sensor faults. A physical model of the WDHS is presented and transformed into a TS model. Then, an FFTC algorithm is developed and applied to a WDHS to demonstrate the effectiveness of this method.