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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.

Introduction In elderly patients, goal-directed haemodynamic therapy (GDHT), depth of anaesthesia monitoring and lung-protective ventilation have been shown to improve postoperative outcomes. The aim of this study was to evaluate current practices concerning strategies of anaesthesia optimisation in patients aged ≥ 75 years. Patients and methods A multicentre observational study was performed from February to May 2015 in 23 French academic centres. On 30 consecutive days in each centre, patients ≥ 75 years with at least one major comorbidity undergoing elective or emergency procedures (femoral-neck fractures surgery, intraperitoneal abdominal surgery or vascular surgery) were included. Patient characteristics and data related to GHDT, management of hypotension, monitoring of temperature and depth of anaesthesia, lung ventilation, point of care haemoglobin testing were collected. Results In total, 807 patients were included. Only 2% of patients [95% CI: 1–3] received GHDT in full accordance with guidelines. Depth of anaesthesia monitoring was largely performed (53% [95% CI: 50–56]). The multifaceted strategy of lung-protective ventilation combining low tidal volumes (6–8 mL/kg), PEEP of 5–8 cm cmH2O, and repeated recruitment manoeuvres, was performed in only 4% [95% CI: 3–5] of patients. A centre effect was a major determinant of variation concerning implementation of these strategies. Discussion In patients’ ≥ 75 years, strategies of anaesthesia optimisation are not in accordance with eligible guidelines. Implementation of these techniques varies independently of factors related to the patient or the type of surgery and may be dependent on the generated constraints.

Resume Chez les patients porteurs de maladies musculaires, l’utilisation du rocuronium pour induction en sequence rapide permet une intubation dans les meilleures conditions de securite, mais induit un bloc neuromusculaire de longue duree. Nous rapportons la diminution de la dose a 0,8 mg/kg chez une patiente atteinte de myopathie facio-scapulo-humerale. Ceci permet de reduire la curarisation residuelle et par consequent la posologie de sugammadex pour l’antagonisation.

In patients with neuromuscular diseases, the use of rocuronium in the general anesthesia rapid sequence induction provides safety intubation conditions, but induces a deep and prolonged neuromuscular blockade. We report dose reduction to 0.8mg/kg for a 47-year-old female with Landouzy-Dejerine myopathy. Therefore, less dose of sugammadex was given to reverse the neuromuscular block.

This paper investigates a method for the state estimation of nonlinear systems described by a class of differential-algebraic equation (DAE) models using the extended Kalman filter. The method involves the use of a transformation from a DAE to ordinary differential equation (ODE). A relevant dynamic power system model using decoupled techniques will be proposed. The estimation technique consists of a state estimator based on the EKF technique as well as the local stability analysis. High performances are illustrated through a simulation study applied on IEEE 13 buses test system.

Unmanned aerial vehicles (UAVs) are increasingly used in military and scientific research. UAVs rely on accurate location information for a variety of purposes including navigation, motion planning and control, and mission completion. UAV INS/GPS localization is generally used based on navigation filter. Extended Kalman filter is largely used to solve the problem of data fusion and localization, however, EKF suffers from the initialization problem and the linearization errors which severely degrade the performance of the UAV localization estimates. In this paper we propose another innovative alternative, which is based on the Hinfin nonlinear filtering to avoid issues linked with classical filtering techniques and getting a significant robustness. This filtering approach is based on the Hinfin robust control theory, results, comparison with the EKF filter and validation on a simulation of a 3D flight scenario are presented.

This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on‐line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.