Ali Zolghadri

Fault diagnosis for LPV systems

In this paper a method for designing robust FDI filters for dynamic systems characterized by LPV (Linear Parameter Varying) polytopic models. A sufficient condition is established to guarantee sensitivity of the fault indicating signals. Robustness constraints against model perturbation and disturbances are taken into account in the design method. A key feature of the proposed method is that the (static) residual structuring matrices are optimized as an integral part of the diagnosis filter. The design problem is formulated as a convex optimization problem and solved using LMI techniques. The proposed method is illustrated on the secondary circuit of a Nuclear Power Plant.

Actuator fault diagnosis for flat systems

This paper describes a robust set-membership-based Fault Detection and Isolation (FDI) technique for a particular class of nonlinear systems, the so-called flat systems. The proposed strategy consists in checking if the expected input value belongs to an estimated feasible set computed using the system model and the derivatives of the measured output vector. The output derivatives are computed using a numerical differentiator. The set-membership estimator design for the input vector takes into account the measurement noise thereby making the consistency test robust. The performances of the proposed strategy are illustrated through a three-tank system simulation affected by actuator faults.

Robust Fault Diagnosis based on Constraint Satisfaction and Interval Continuous-time Parity Equations

Abstract This paper deals with fault diagnosis for a class of nonlinear continuous-time systems based on an interval extension of continuous-time parity equations. The parity equations are designed based on ordinary differential equations modeling some links between the successive output and input vector derivatives. The noisy inputs and outputs are evaluated through a high-order sliding-mode differentiator. The redundancy relations are formulated as a Constraint Satisfaction Problem (CSP) with faults as unknown variables. In this case, a reliable estimation of the fault magnitude can be performed by solving the CSP using interval techniques. This technique is well suited to deal with actuator faults as well as parametric or sensor faults. However, in this paper, we choose to focus on actuator faults and the performance of the proposed approach is illustrated through simulations on oscillatory failure detection in aircraft surface servo loops. input observer, fault reconstruction, consistency techniques, nonlinear systems.

On the formal characterization of reduced-order flat outputs over an ore algebra

In this paper, a formal and effective methodology is proposed to characterize a subset of reduced-order flat outputs for nonlinear implicit systems described in the differential geometric framework of jets of infinite order. The proposed methodology includes an original management of the degrees of freedom left by the polynomial matrices approach. First, a reduced-order basis of the free left D-module, finitely presented by the variational system, is obtained by computing some reduced-order bases of the left nullspace of Ore polynomial matrices and by using the weak Popov form over an Ore algebra. Under the assumption that the latter module is free, a structural constraint is introduced in the generalized Cartan moving frame structure equations in order to obtain a subset of reduced-order flat outputs.

Change detection in flat systems by constraint satisfaction techniques

Abstract This paper deals with robust change detection of flat systems. The problem is formulated in terms of constraint satisfaction problems taking advantage of the flatness property. A consistency test is developed based on a comparison of an estimated feasible set and the expected value of the input vector. This strategy consists in invalidating models of the plant that are not consistent with the set of observations provided by the system sensors. The set-membership estimator design for the input vector takes into account the model uncertainties and disturbances, which makes the consistency test robust against such perturbations. The robustness of the proposed strategy is illustrated by simulations using several sensor/actuator faults scenarios.

Robust state estimation for flat systems using set-membership techniques

Abstract Robust state estimation for a subclass of nonlinear systems is considered in this paper through a comparison of two methodologies. The comparison is based on modus operandi complexity, calculation time and interval observation quality. The first approach is based on a fundamental property of flat systems which states that the state of the system can be written as a function of the so-called flat outputs and their derivatives up to some order. Moreover, it is only assumed that the measurement noise and the disturbances are bounded without any additional information such as stationarity, uncorrelation or type of probabilistic distribution. Therefore, the interval state estimation is expressed as a set inversion problem which is solved using interval analysis. The second approach is based on coordinate transformation in order to obtain a partially linear cooperative presentation of the nonlinear system for which a closed-loop interval observer is designed. Both methods ensure to enclose the set of system states that is consistent with the model and the measurement noise bounds. The performance of each technique is discussed. Numerical examples are given throughout the paper to illustrate the performances of the proposed techniques.