The declination of the dependability requirements of the complex systems is one of the most critical processes of systems conception as well as systems engineering. We consider that these dependability requirements must be globally considered at the complete system level if they are to be respected before being allocated on the components of the system. Our approach is based on a three-dimensional decomposition model of the system whose purpose is to describe the way in which the system operates in order to deduce its dysfunction that serves as a basic support for the declination of the dependability requirements. The proposed method is applied to a nitric acid cooling system before being introduced into a chemical reactor.

This article describes how fault tolerance is an essential issue for many WSN (Wireless Sensor Network) applications such as wildlife monitoring, battlefield surveillance and health monitoring. It represents a great challenge for researchers regarding to the characteristics of sensor nodes which are prone to failures due essentially to their limited resources. Faults may occur, not only when sensor nodes exhaust their energy, but also when the congestion phenomenon emerges, because of a high traffic in the network and limited storage capacity of the sensor nodes. In order to support fault tolerance in WSNs, the authors propose a new scheme which incorporates a link quality estimation algorithm and a congestion detection mechanism to enable nodes that present high quality links to be chosen for routing in a non-congested area in case of faults. Evaluations through simulations under NS2 show that our proposed protocol tolerates faults with a minimum cost relatively to HEEP protocol and improves network’s performances comparatively to other fault tolerant protocols such as EF-LEACH.

The selectivity is one of the main challenges to develop a gas sensor, the good chemical species detection in a gaseous mixture decreasing the missed detections. The present paper proposes a new solution for gas sensor selectivity based on artificial neural networks (ANNs) and fuzzy logic (FL) algorithm. We first use ANNs to develop a gas sensor model in order to accurately express its behavior. In a second step, the FL and Matlab environment are used to create a database for a selective model, where the response of this one only depends on one chemical species. Analytical models for the gas sensor and its selective model are implemented into a Performance Simulation Program with Integrated Circuit Emphasis (PSPICE) simulator as an electrical circuit in order to prove the similarity of the analytical model output with that of the MQ-9 gas sensor where the output of the selective model only depends on one gas. Our results indicate the capability of the ANN-FL hybrid modeling for an accurate sensing analysis.

This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.

The disturbances are the significant issue for the trajectory tracking of mobile robots. Therefore, an adequate control law is presented in this paper and this one is based on Global Terminal Sliding Mode (GTSM) with fuzzy control. This control law aims to guarantee the avoidance of the kinematic disturbances which are injected in the angular and linear velocities, respectively. Moreover, the dynamic model based on exponential reaching law is presented to avoid the uncertainties. The control law provides the asymptotic stability by taking into account the fuzzy rules and Lyapunov theory. Thus, the chattering phenomenon should be avoided. The simulation works prove the robustness of the proposed control law by considering the disturbances function and the robot can follow the desired trajectories.