State and unknown input estimation via a proportional integral observer with unknown inputs

International audienceThis paper deals with the problem of fault detection and identification in noisy systems. A proportionnal integral observer with unknown inputs is used in order to estimate the state and the faults which are assumed as unknown inputs. 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

International audienceThis 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

An approach of faults estimation in Takagi-Sugeno fuzzy systems

International audienceIn 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 unknown input. Considering actuator fault as an unknown input, one can use a method of estimation of unknown inputs. The noise effect on the state and fault estimation is also minimized

State and sensor faults estimation via a proportional integral observer

International audienceThis paper deals with the problem of fault detection and identification in noisy systems. A proportional integral observer with unknown inputs is used in order 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. This reconstruction is made by the use of a proportionnel integral observer. 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

Fault tolerant control for nonlinear systems described by Takagi-Sugeno models

National audienceIn this paper the problem of active fault tolerant control (FTC) in noisy systems is studied. The proposed FTC strategy is based on the known of the fault estimate and the error between the faulty system state and a reference system state. A proportional integral observer is used in order to estimate the state and the actuator faults. The obtained results are then extended to nonlinear systems described by nonlinear Takagi-Sugeno models. The problem of conception of the proportional integral observer and the FTC strategy is formulated in linear matrix inequalities (LMI) which can be solved easily. Simulation examples are given to illustrate the proposed method for the linear and nonlinear systems

Sensor fault estimation for nonlinear systems described by multiple models

International audienceThis paper deals with the problem of sensor fault estimation for linear and nonlinear systems. Thanks to the introduction of an augmented generalized state vector, including the original state vector and a filtered output of the system, the sensor fault ap- pears as an unknown input. Therefore, an adaptive proportional integral observer is used to estimate simultaneously the state of the system and the unknown input. In order to provide some robustness properties, the disturbance effect on the state and fault estimation errors is minimized in an L2 framework. State and sensor fault estimation is firstly presented for linear systems and is next extended to nonlinear systems described by Takagi- Sugeno models

Design of an adaptive fault tolerant control: case of sensor faults

International audienceThis paper presents a method of design of a sensor faults tolerant control. The method is presented for the case of linear systems and then for the case of non linear systems described by Takagi-Sugeno models. The faults are initially estimated using a proportional integral observer. A mathematical transformation is used to conceive an augmented system in which the sensor fault appear as an unknown inputs. The synthesized control depends on the estimated faults and the error between the state of a reference reference and the faulty system state. The fault tolearnt control is conceived using the augmented state. The conditions of the observer convergence and of the control existence are formulated in terms of Linear Matrix Inequalities (LMI). The formulation in LMI shows that the synthesis of the control and the observer can be independently made. For both cases (linear and non linear) The theoretical results are validated by their application to a noisy system affected by sensor faults

Linear Algebraic Formalism for State Estimation of Labeled Petri Net

International audienceIn this paper we present an algebraical approach for the state estimation of discrete event systems, partially observed and modeled by labeled Petri nets. Our estimation approach is based on the on-line observation of firing occurrences of some transitions to determine the set of all possible actual sequences and markings. This approach has been used to minimize the computation time and space for the state estimation using an off-line procedure