Boulaid Boulkroune

ℋ −/ℋ ∞ fault detection filter for a class of nonlinear descriptor systems

This article addresses the problem of designing a robust fault detection filter (RFDF) for a class of nonlinear descriptor systems whose nonlinear term is globally Lipschitz and linear term is described by a linear parameter varying form. The effect of perturbations on the residuals is minimised using the ℋ∞ norm while the sensitivity performance is guaranteed using the ℋ− index. Thanks to a particular Lyapunov function: which depends on the faults and the filter design problem is formulated as a convex optimisation problem solved via linear matrix inequality techniques. Performances of the proposed filter are shown through the application of an activated sludge process model.

Nonlinear unknown input observer design for diesel engines

In this paper, the problem of designing a nonlinear unknown input observer (NIUO) for a general class of nonlinear systems in the presence of disturbances is addressed. This approach combined two criteria : the modified H∞ criterion and mean value theorem. The first criterion allows to solve the problem of unknown input observer design in the presence of disturbances. While the second is used to express the nonlinear error dynamics as a convex combination of known matrices with time varying coefficients. A sufficient conditions for the existence of the unknown input observer is derived. The observer gains are obtained by solving the linear matrix inequality (LMI). The advantage of the proposed method is that no a priori assumption on the unknown input is required and also can be employed with a wider class of nonlinear systems. Performances of the proposed approach are shown through the application to a diesel engine model.

H∞ circle criterion observer design for Lipschitz nonlinear systems with enhanced LMI conditions

This note deals with observer design for Lipschitz nonlinear systems via LMI. A new LMI condition is proposed to solve the problem of H∞ circle criterion observer design. This enhanced LMI is less conservative than those proposed in the literature for the same class of systems by using the same methodology. Indeed, thanks to a new and judicious use of the Youngs relation, additional degree of freedoms are included in the LMI, contrarily to some recent results, which turn to be particular cases of what we proposed in this paper. This additional decision variables allow to improve the feasibility of the proposed LMI. A numerical example is given to show the effectiveness of the proposed methodology.

Nonlinear unknown input observer for intake leakage estimation in diesel engines

Intake leakage estimation problem in diesel engines is addressed in this paper. The need to guarantee high-performance engine behavior and in particular to respect the environmentally-based legislative regulations motivates this work. Today, diesel engines monitoring has become an obligation and a necessity. In this paper, based on a Takagi-Sugeno (TS) fuzzy model of the engine, a nonlinear unknown input observer (NUIO) is used to estimate the intake leakage mass flow rate. The advantage of the proposed method is that no a priori assumption on the intake leakage size is required. The diagnosis system is successfully evaluated using an advanced diesel engine professional simulator AMEsim(LMS).

State and unknown input estimation for nonlinear singular systems: application to the reduced model of the activated sludge process

An estimation of the state and the unknown inputs of the reduced nonlinear model of an activated sludge process using the Extended Kalman Filter (EKF) is proposed. First, we present the reduced nonlinear model. This model contained five state variables and four unknown inputs. For satisfying the rank condition for the construction of an EKF, one unknown input has been approximated and the daily mean value of another unknown input has been used. Then, to estimate conjointly the state and the unknown inputs, the reduced nonlinear system is transformed to a nonlinear singular system. High performances of the proposed observer will be shown through the simulation results.

Robust sensor fault detection and isolation for a doubly-fed induction generator

The problem of multiplicative fault detection and isolation (FDI) in the current sensors of a doubly-fed induction generator (DFIG) is considered in the presence of model uncertainty. A residual generator based on the DFIG model is proposed using the structure of the classical generalized observer scheme (GOS). However, each observer in this scheme is replaced by a robust ℋ−/ℋ∞ fault detection filter followed by a Kalman-like observer. It turns out that the fault detection/isolation problem then amounts to detecting the appearance of sine waves superimposed to specific components of a white noise vector. A Generalized Likelihood Ratio Test (GLRT) is developed to solve the problem. The complete FDI system is validated through simulations on a controlled DFIG.

Stabilization of LPV positive systems

This paper considers the stabilization issue for continuous-time linear parameter varying (LPV) positive systems. The time varying parameters are known and are modeled as belonging to the simplex set. The proposed stabilization approach relies on a parameter dependent Lyapunov function combined with a subtle choice of a slack variable that is not necessary diagonal. In fact, due to the positivity constraint on the closed-loop system the slack variable is chosen to be a Metzler matrix. Indeed, the particular case when the slack matrix is diagonal may work but the resulting stabilization conditions can be conservative. This fact is illustrated by a comparison example.

Robust fault diagnosis for a class of nonlinear descriptor systems

The paper addresses the problem of designing a robust fault detection filter (RFDF) for a class of nonlinear descriptor systems whose nonlinear term are globally Lipschitz and linear term described by a linear parameter varying (LPV) form. The influence of perturbations on the residuals is minimized using the H∞ norm while the sensitivity performance is guaranteed using the H−. index. Based on the Lyapunov stability theory, the design problem is formulated as a convex optimization problem and solved via linear matrix inequality (LMI) techniques. Performances of the proposed filter are shown through the application to an activated sludge process model.

Robust fault detection and isolation in current sensors of doubly-fed induction generators

Abstract The problem of fault detection and isolation (FDI) in the current sensors of a doubly-fed induction generator (DFIG) in the presence of model uncertainty is discussed in this paper. The proposed residual generator is inspired by the structure of the classical generalized observer scheme (GOS). However, each observer in this scheme is replaced by a robust ℋ _ /ℋ ∞ fault detection filter (FDF) followed by a Kalman-like observer. The latter filters a specific frequency in the output generated by the ℋ _ /ℋ ∞ FDF. A multi-CUSUM (Cumulative Sum) algorithm is used as decision system. The approach is validated through simulations of a controlled DFIG.

Circle criterion-based ℋ∞ observer design for Lipschitz and monotonic nonlinear systems – Enhanced LMI conditions and constructive discussions

A new LMI design technique is developed to address the problem of circle criterion-based