Abdel Aitouche

Optimal design of fault tolerant sensor networks

The selection of measurements is one of the most important problems in the design of process instrumentation. This paper deals with the design of sensor networks such that the observability of the variables, which are necessary for the process control, remains satisfied in the presence of sensor failures. Pseudo-minimal and minimal sensor sets are organized into an oriented graph which contains all the possible reconfiguration paths for which those variables remain observable. A bottom-up analysis of this graph allows one to compute reliability functions which evaluate the robustness of the observability property with respect to sensor failures. The design of optimal sensor networks thus resumes to finding pseudo-minimal sensor sets such that the mean time before losing the observability property is larger than a pre-defined value.

Fault Tolerant Control on an Electric Vehicle

The main purpose of this paper concerns a fault tolerant control applied on an electric vehicle known as RobuCar which is a 4times4 electric vehicle, with four electromechanical wheel systems. Fault tolerant control (FTC) is attended to continue the system operation in the presence of several faults in order to remain the security of the system. Active fault tolerant control (AFTC) approach needs fault detection and isolation (FDI) algorithm to detect and identify the fault with minimum false alarm, missed alarm and time delay ... Only healthy components are used to reconfigure the control law of the system. RobuCar is particularly adapted to receive a fault tolerant module since it is composed of redundant components.

Robust Scheduler Fuzzy Controller of DFIG Wind Energy Systems

This paper addresses the robust fuzzy scheduler controller (RFSC) for nonlinear systems which is robust enough to stabilize a nonlinear system with parametric uncertainties, wind disturbance, and give an acceptable closed-loop performance in the presence of state variables unavailable for measurements. The Takagi-Sugeno (TS) fuzzy model is adopted for fuzzy modeling of the nonlinear system. The concept of parallel distributed compensation (PDC) is employed to design fuzzy control from the TS fuzzy models. Sufficient conditions are formulated in the format of linear matrix inequalities (LMIs). The proposed controller design methodology is finally demonstrated through the model of wind energy systems (WES) with a doubly-fed induction generator (DFIG) to illustrate the effectiveness of the proposed method. The proposed algorithm maximizes the produced power and is able to maintain a stable system during the parameter uncertainties.

Fuzzy Scheduler Fault-Tolerant Control for Wind Energy Conversion Systems

In this paper, a new fuzzy scheduler fault-tolerant control method is proposed for nonlinear systems subject to sensor faults, parameter uncertainties, wind disturbance, and state variables unavailable for measurements. An algorithm based on the reconfiguration mechanism is then investigated for detection, isolation, and accommodation of sensor faults. The Takagi-Sugeno fuzzy model is employed to represent the nonlinear wind energy conversion system, and then a model-based fuzzy scheduler controller design uses the concept of general-distributed compensation. Sufficient stability conditions are expressed in terms of linear matrix inequalities, which can be solved very efficiently using convex optimization techniques. The proposed algorithm maximizes the produced power and minimizes the voltage ripple and is able to maintain stability of the system during sensor faults, wind disturbance, and parameter uncertainties. The design procedures are applied to a dynamics model of the typical wind energy conversion system to illustrate the effectiveness of the proposed control technique.

Robust nonlinear observer design for actuator fault detection in diesel engines

Abstract This paper is concerned with actuator fault detection in nonlinear systems in the presence of disturbances. A nonlinear unknown input observer is designed and the output estimation error is used as a residual for fault detection. To deal with the problem of high Lipschitz constants, a modified mean-value theorem is used to express the nonlinear error dynamics as a convex combination of known matrices with time-varying coefficients. Moreover, the disturbance attenuation is performed using a modified H∞ criterion. A sufficient condition for the existence of an unknown input observer is obtained using a linear matrix inequality formula, and the observer gains are obtained by solving the corresponding set of inequalities. The advantages of the proposed method are that no a priori assumption on the unknown input is required and that it can be applied to a large class of nonlinear systems. Performances of the proposed approach are shown through the application to a diesel engine model.

Fault Detection of Actuator Faults for Electric Vehicle

The main purposes of this paper concern: fault generation of nonlinear analytical redundancy for actuator faults of electric vehicle. These relations are used to detect and isolate actuator fault based on the elimination of the unknown variables of the system. Each residual, obtained by elimination theory, is sensitive to only one actuator fault thanks decentralization of the electric vehicle known as RobuCar. Structural analysis is used before to give the information of detectability and isolability of faulty components. Simulations of steering and traction engine faults in driving situation are showed.

Adaptive observer for Intake leakage detection in diesel engines described by Takagi-Sugeno model

The problem is motivated by the need to guarantee high-performance engine behavior and in particular to respect the environmentally-based legislative regulations. The complexity of the intake systems of this type of engine makes this task particularly arduous and requires constantly monitoring and diagnosing the engine operation. First of all, a mean value model of a diesel engine subject to leakage in the intake manifold is presented and transformed into Takagi-Sugenos model. Then, an adaptive observer for intake leakage estimation is developed. The proposed observer approach allows to estimate simultaneously the system states and a variable that is directly related to the presence of leakage. The diagnosis system is successfully evaluated using an advanced diesel engine professional simulator AMEsim(LMS).

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.

Structural analysis for air path of an automotive diesel engine

The paper focuses on the application of structural analysis based on mathematical model of air path of an automotive Diesel engine to realize the objective of fault detection and isolation. The model of the air path system was transformed into structural state equations in order to simplify the procedure of structural analysis. The paper is concerned on how to generate nonlinear analytical redundancy relations in view to study the monotorability of the system. The results show that all specified faults can be detected but some of therm can not be isolated.

Control strategy for the air path dynamic system

This paper focuses on the control of diesel HCCI engine air path. We propose a control strategy based on Recursive Model Free Controller (RMFC). This kind of controller is simple to design and it needs only the outputs of the system. On other hand, in order to estimate the state variables, an observer is used and its convergence is performed by using Lyapunov asymptotic stability and is formulated in the format of Linear Matrix Inequalities (LMI) to obtain observer gains. The proposed approach while possessing a simple structure proves a good performance in term of static state error with an accepted setting time. The proposed strategy has been successfully evaluated using a professional advanced diesel engine simulator AMEsim(LMS) platform in co-simulation with SIMULINK.