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This work deals with the fault estimation of actuator fault for a polymer electrolyte membrane fuel cell (PEMFC) using descriptor approach. The main contribution of this paper is to consider unknown inputs and time delay in the output. Delay-dependent sufficient conditions for the design of an unknown input observer with time delays are given in terms of linear matrix inequalities (LMIs). For that purpose, an unknown input observer (UIO) for delayed LPV model is designed. Firstly, by considering the faults as auxiliary states, we obtain an augmented LPV system. An algorithm scheme is developed in order to compute the UIO proposed for the augmented system with actuator faults, secondly. Basing on the Lyapunov theory, the convergence and the stability of the UIO are analyzed. Finally and in order to apply the proposed fault diagnosis approach, the time delay is related to the mass flow oxygen in the anode of a PEMFC and actuator faults such as the compressor outlet mass flow rate and the return manifold outlet orifice are designed. Simulation results prove the effectiveness of this approach.

This paper presents a sensor fault estimation scheme for polymer electrolyte membrane (PEM) fuel cells using Takagi Sugeno (TS) fuzzy model. First, PEM fuel cell systems with sensor faults are modelled by TS fuzzy model. Next, by adding a first order filter, an augmented TS fuzzy system with actuator fault is obtained. Then, for the augmented system, an unknown input observer (UIO) and a fault estimator are developed. The UIO gains are computed by solving linear matrix equalities (LMEs) and linear matrix inequalities (LMIs). The UIO convergence and stability are analyzed and the performances of the proposed fault estimation scheme is demonstrated by numerical simulations for a PEM fuel cell system with return manifold pressure and hydrogen mass sensors.

In this paper we use a Takagi–Sugeno (T–S) fuzzy approach to run a photovoltaic generator (PV) at its maximum power (MPPT). To control the power delivered by the generator, a boost converter dc/dc is used. By acting on the duty cycle of the converter, the photovoltaic generator operates at its maximum power. A T–S fuzzy controller is used to determine the duty cycle of the converter. The analysis of the stability of the proposed system as well as the rejection of perturbations is assured using the Lyapunov theory and the H-infinite method. The computation of the gains of the controller is obtained from the use of Linear Matrix Inequality (LMI). The control performances of the system are shown from the results of the numerical simulation obtained using the Matlab/Simulink.

This paper presents an actuator fault and disturbance estimation strategy using Takagi-Sugeno (TS) fuzzy model. In this approach, using a coordinate transformation, the TS fuzzy system is decomposed into three different modules: state subsystem without fault and disturbance, disturbance subsystem without fault, fault subsystem without disturbance. After the transformation, the fault and disturbance can be decoupled and calculated from the input and output signals and estimation state. The convergence of TS fuzzy observer is analyzed and proved. In order to verify the performance of the proposed approach, a wind turbine system with actuator fault and disturbance have been tested, the simulation results illustrate the efficiency of the proposed strategy.

Wind turbine system due to its aerodynamic and generator components is a nonlinear and strongly coupled system. These characteristics influence the efficiency and performance of the systems output. In order to improve the efficiency of Maximum Power Point Tracking (MPPT) and to produce more green energy, a novel control strategy which is based on RBF neural network is designed. The proposed algorithm depends on the optimal tip speed ratio and adjusts rapidly the output of torque with the random wind. Finally, to validate and demonstrate the performance of the proposed method, the FAST and Matlab/Simulink are used to test under different conditions of Wind.

The problem of Robust Simultaneous Fault Detection and Control (RSFDC) approach for a class of uncertain system with state time delay, was investigated in this paper. Based on a dynamic filter with state feedback controller, H∞ formulation of the RSFDC problem was presented. In fact, the idea given herein, is based on applying the advantages of dynamic observers, which lead to some sufficient conditions for solvability of the RSFDC problem in terms of LMI feasibility conditions. The effectiveness of the design technique is illustrated via an example.

This paper presents an active fault tolerant control (AFTC) strategy for preserving the path tracking for an autonomous 2 wheel-steering 4 wheel-driving (2WS4WD) electrical vehicle in the presence of an actuator fault. It is based on a decentralized fault tolerant control strategy for overactuated systems. The strategy consists of generating new REFERENCEs for redundant actuators, which are only used when faults are detected, and tracking these REFERENCEs. For a 2WS4WD vehicle, five actuators are used in normal situations to ensure the vehicle path tracking: the front-wheel steering actuator and the four traction actuators. However, in faulty situations, the vehicle's rear-wheel steering actuator is controlled in order to preserve the system's path tracking and to ensure the desired performance. The elaborated control law that is used to control the rear-wheels steering system is composed of 2 interconnected control loops: an outer loop and an inner loop. This decentralized control strategy tolerates the traction and the front-wheel steering actuator faults. The main advantage of the proposed fault tolerant strategy is that the faults can be compensated by computing online new REFERENCEs for the control loops without changing the initial controllers. This strategy provides the necessary time for a diagnosis system to precisely isolate the faulty actuator. This method is tested and validated on a realistic vehicle dynamic model co-simulated using CarSim and Matlab-Simulink softwares.

International audience; Editorial of a special issue

In this paper, new robust fuzzy scheduler fault tolerant control is proposed to tackle multivariable nonlinear systems subject to sensor faults, actuator faults and parameter uncertainties. Takagi–Sugeno fuzzy model is employed to represent the nonlinear wind energy systems, and then a model-based fuzzy scheduler controller design use the concept of general-distributed compensation. Takagi–Sugeno fuzzy systems are classified into three families based on the input matrices and a fault tolerant control synthesis procedure is given for each family. In each family, sufficient conditions are derived for robust stabilization, in the sense of Lyapunov method and Taylor series stability, for the Takagi–Sugeno fuzzy system with parametric uncertainties, sensor faults, and actuator faults. The sufficient conditions are formulated in the format of linear matrix inequalities. The effectiveness of the proposed controller design methodology is finally demonstrated through a wind energy system with doubly fed induction generators to illustrate the effectiveness of the proposed method.

The paper presents a fault tolerant control (FTC) strategy for maintaining the lateral stability of a 4WS4WD autonomous vehicle in presence of an unknown component fault. It is designed using the flatness theory and the backstepping technique, and consists in actively controlling the vehicle's rear-wheel steering system. In a normal situation, only the front-wheel steering actuator is used since it is able to ensure the desired tracking performances on its own. However, for different faulty scenarios, it is needed to use actively the vehicle rear-wheel steering system in order to preserve the system's lateral stability. We prove that, by using this strategy, faults on the traction and the steering systems can be tolerated. The main advantage of our fault tolerant scheme is that these faults can be compensated by online computing new references for the control loops without changing the controller, providing the necessary time for a diagnosis system to precisely isolate the faulty component. This method is tested and validated on a realistic vehicle dynamic model co-simulated using CarSim and Matlab-Simulink softwares.

This paper deals with the fault tolerant control of an autonomous four-wheels-steering vehicle. In normal situations, the trajectory of the vehicle is controlled using the only front wheels steering actuator. It is known that the stability of the vehicle may be lost in specific faulty scenarios of the front wheels steering mechanism. An original approach is proposed, where rear wheels steering actuator is actively controlled in order to ensure the trajectory tracking in presence of front wheels steering faults. A dynamic references generator for rear wheels steering controller is designed using the flatness property of the studied system and is activated as soon as a fault is detected. This method is tested and validated on a realistic vehicle dynamic model simulated using CarSim software.

International audience; In this paper, a new FTC strategy of the diesel engine airpath subject to intake manifold leakage described by Takagi-Sugeno model is presented. The proposed FTC design scheme integrates the state estimation, the leakage identification and the state feedback control law, to guaranty the stabilization of the faulty plant. State vector and leakage are estimated by an adaptive observer. The gains of the adaptive observer and feedback control law are obtained by solving a linear matrix inequality derived from the Lyapunov theory. The effectiveness and performances of the proposed approach are illustrated using the professional advanced diesel engine simulator AMEsim(LMS) platform in co-simulation with SIMULINK software.

Fuel cells are electrochemical energy conversion devices that convert hydrogen and oxygen into water, producing electricity and heat in the process. Fuel cell is one of the best alternatives of fossil energy. Recently, the research community of fuel cell has shown a considerable interest for diagnosis in view to ensure safety, security, and availability when faults occur in the process. These faults must be detected early and sometime estimated and accommodated. Therefore, they are vulnerable to faults that can cause the stop or the permanent damage of the fuel cell. To guarantee the safe operation of the fuel cell systems, it is necessary to use systematic techniques to detect and isolate faults. The present paper presents a survey and analysis of different techniques used for diagnostic of Fuel Cell Stack System.

This paper is concerned with the design of sensor networks which keep the system observable 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 the system remains observable. A bottom-up analysis of this graph allows to compute redundancy degrees which evaluate the robustness of the observability property with respect to sensor failures. The design of sensor networks thus resumes to finding pseudo-minimal sensor sets with given redundancy degrees.

Fuel Cell Systems (FCS) offer clean and efficient energy production and are currently under intensive development by several manufacturers. The viability and efficiency of this expensive technology depend on real time Fault Detection and Isolation (FDI). For this task, most (80%) of the developed methods deals with no model based (experimental or qualitative approach) despite of well known drawback of those approaches. This is due of complexity of physical phenomena occurring in such process. Use of bond graph tool as a multidisciplinary energetic approach is well suited for modeling of FCS. The innovative interest in the presented paper is the use of obtained graphical non-linear behavioral model not only for simulation but for diagnosability analysis (Which faults can be detected and isolated before design) and formal fault indicators generation without need of calculation. Functional, structural and causal properties of bond graphs are exploited for this.

Background Ventricular unloading decreases cardiac ventricular mass. This loss of ventricular mass can be due to either atrophy (a reversible process) or apoptosis (an irreversible process) of the cardiac myocytes. We investigated the effect of ventricular unloading on atrophy and apoptosis of cardiac myocytes, using working and nonworking transplant heart models in rats. Materials and methods ACI rats underwent heterotopic heart transplantation with two different techniques to create working and nonworking cardiac grafts. Cardiac grafts were harvested at different time points after transplantation. TUNEL, caspase-3 assay, and electron microscopy were used to assess the degree of apoptosis while cellular atrophy was estimated by calculation of the cytoplasmic index (CI = mean sectional cytoplasmic area/nucleus). Results Ventricular mass reduction was more pronounced in nonworking than in working hearts (P Conclusions These data suggest that cellular atrophy is the primary mechanism that accounts for myocardial weight reduction following ventricular unloading. The inference is that ventricular unloading by ventricular assist devices may not cause permanent loss of cardiac myocytes, thus allowing for functional recovery.

Introduction: Given its multifactorial etiology, the relative contribution of anti-donor cellular and humoral immune responses in the pathogenesis of chronic rejection is as yet ambiguous. We hypothesized that alloreactive T and B cells play a seminal role in the development of this lesion. Methods: To address this hypothesis, RAG-2 −/− mice were used as donors and recipients in a well-established murine model of aortic transplantation. Grafts were transplanted across the following groups: Group I: C3H → C3H; Group II: Wild-type [WT] 129Sv (H-2 b ) → C3H (H-2 k ); Group III: C3H → WT 129Sv; Group IV: 129SvEv RAG-2 −/− → C3H; and Group V: C3H → 129SvEv RAG-2 −/− . Grafts were harvested at d40 to 146 post-transplantation for morphologic and immunohistochemical analyses and semi-quantitative RT-PCR was employed to evaluate the intragraft mRNA expression of various immune mediators. Mixed lymphocyte reaction and complement-mediated alloantibody cytotoxicity assays were performed to determine anti-donor proliferative and humoral responses, respectively. Results: Unlike that across the syngeneic combination (Group I), marked intimal thickening with corresponding luminal narrowing was observed in the majority of the aortic allografts (Groups II–IV). On the contrary, the morphology of C3H aortic allografts harvested from the majority of the RAG-2 −/− was remarkably preserved. Correspondingly, anti-donor proliferative and humoral immune responses were undetectable in C3H → RAG-2 −/− recipients as was the intragraft mRNA expression of the Th 1 and the Th 2 -type cytokines. Conclusions: Taken together, these data suggest that in this murine model of aortic allotransplantation, donor-specific cellular and humoral responses play a dominant role in the initiation and perpetuation of chronic rejection.

Supervision system requires not only a high-speed system, but also high reliability (i.e. fault-tolerant systems), high flexibility (generic design) and high scalability (distributed design). Industrial applications have revealed these absolute needs that distinguish modern architectures from the classical ones. Combining top-level Research from three fields - automation, distributed artificial intelligence (DAI) and distributed computer science, a supervision architecture is designed that fits all these criteria. Automation provided a functional description of the smart instruments, DAI, the modularity of multi-agents systems, and distributed database technologies, a reliable protocol for transactional processing.

Objective The toxic dose of irradiation required to achieve stable mixed hematopoietic chimerism is the major limitation to its clinical application in transplantation and other nonmalignant conditions such as hemoglobinopathies. This study examines the additive effect of costimulatory blockage, to our previously described tacrolimus-based conditioning regimen, in further reducing the dose of total-body irradiation to achieve stable mixed chimerism in rats. Methods Fully mismatched, 4- to 6-week-old ACI and Wistar Furth rats were used as donors and recipients, respectively. Recipients were administered CTLA4-Ig 2mg/kg/day (alternate days) in combination with tacrolimus 1 mg/kg/day (daily) from day 0 through day +10, anti-lymphocyte serum 10 mg at day +10 (single dose), and total-body irradiation ranging from 100–600 cGy, prior to bone marrow transplantation (day 0) with 100 × 10 6 of T-cell-depleted bone marrow cells. Levels of donor chimerism were determined over a period of 12 months. Results The short course of CTLA4-Ig, tacrolimus, and ALS led to dramatic engraftments at reduced doses of irradiation: 100% (5/5) and 93% (13/14) of the animals developed mixed chimerism at 400 cGy and 300 cGy, respectively. At 300 cGy, recipients exhibited durable, multilineage mixed chimerism at 365 days with donor cells ranging from 19–42% (mean 23.4%) with no evidence of graft-vs-host disease. These mixed chimeras exhibited in vitro (mixed lymphocyte reaction) and in vivo (skin grafts) donor-specific tolerance. Conclusion This study suggests that addition of costimulatory blockade to a tacrolimus-based conditioning regimen reduces the dose of irradiation required to achieve stable multilineage chimerism in rats.

This paper is concerned with the analysis of fault tolerance properties with respect to actuator failures. Controllability, individual controllability and pseudo-controllability indexes are used to build minimal and pseudo-minimal actuator sets, considering both global and partial state controllability. Pseudo-minimal and minimal actuator sets are organized into an oriented graph which contains all the actuator sets such that the system is controllable. The analysis of this graph allows to compute redundancy degrees, which evaluate the system fault tolerance with respect to actuator failures.

This paper deals with the Fault Detection and Isolation (FDI) on industrial systems such as steam generator process whose model is non linear. The method is based on Analytical Redundancy Relations which are generated from a bipartite graph These relations are used to detect and isolate Sensor Fault using structural analysis, based on the elimination of the unmeasured variables of the system. Simulations are used to detect and isolate multiple sensor faults in order to prove the efficiency of our method