This paper investigates the problem of robust fault detection observer design for nonlinear Takagi-Sugeno models with unmeasurable premise variables subject to sensor faults and unknown bounded disturbance. The main idea is to synthesize a robust fault detection observer by means of a mixed H =H 1 performance index. The considered observer is used to estimate jointly states and faults. Using the technique of descriptor system representation, we proposed a new less-conservative approach in term of a linear matrix inequality (LMI) by considering the sensor fault as an auxiliary state variable. A solution of the problem is obtained by using an iterative LMI procedure.In the field of diagnosis, this assumption forces to design observers with weighting functions depending on the input u.t /, for the detection of the sensors faults, and on the output y.t /, for the detection of actuator faults. Indeed, if the decision variables are the inputs, for example, in a bank of observer, even if the i th observer is not controlled by the input u i , this input appears indirectly in the weighting function and it cannot be eliminated. For this reason, it is interesting to consider the case of weighting functions depending on unmeasurable premise variables, such as the state of the system. This case makes it possible to handle a large class of physical systems [21][22][23].Using descriptor approach, this work dealt with the problem of fault detection observer for Takagi-Sugeno (T-S) model affected by both sensor faults and bounded disturbances. Although many papers have dealt with the problem of observer design for descriptor systems, only a few works have been carried out for simultaneous disturbance rejection and fault detection algorithms [1]. Compared with existing fault estimation schemes [24,25], the given descriptor observer approach leads to more suitable observer design, which is applicable to diagnosis of more general faults. The proposed procedure has the advantage, over the ones proposed on [26,27], to estimate different faults types, whereas the proposed method in [26] is only able to estimate step faults. The problem formulation in a descriptor form allows also to estimate state and sensor faults simultaneously.This paper aims to extend the results proposed in [4] to T-S models with unmeasurable premise variables. The present work illustrates the design of a fault detection observer for T-S model affected by sensor faults and unknown bounded disturbances. The observer gains and the residual weighting matrix are obtained through the minimization of an H 1 norm and the maximization of an H norm. The main objective is to design a fault detection observer such that the resulting residual has the best robustness to disturbances and the best sensitivity to faults. Sufficient conditions are expressed in terms of linear matrix inequalities (LMIs), and an iterative algorithm is provided to get the solution. This algorithm can be solved effectively using numerical optimization techniques.This paper is organized as follows. In ...
This paper describes an agent based approach for simulating the control of an air pollution crisis. A Gaussian Plum air pollution dispersion model (GPD)is combined with an Artificial Neural Network (ANN) to predict the concentration levels of three different air pollutants. The two models (GPM and ANN) are integrated with a MAS (multi-agent system). The MAS models pollutant sources controllers and air pollution monitoring agencies as software agents. The population of agents cooperates with each other in order to reduce their emissions and control the air pollution. Leaks or natural sources of pollution are modelled as uncontrolled sources. A cooperation strategy is simulated and its impact on air pollution evolution is assessed and compared. The simulation scenario is built using data about Annaba (a city in North-East Algeria). The simulation helps to compare and assess the efficiency of policies to control air pollution during crises, and takes in to account uncontrolled sources.
Abstract. Environmental issues, specifically pollution are considered as major concerns in many cities in the world. They have a direct influence on our health and quality of life. The use of computers models can help to forecast the impact of human activities on ecosystem equilibrium. We are interested in the use of MAS (Multi-Agent System) for modelling and simulating the environmental issues related to pollution. In this paper, we present a review of recent studies using a MAS approach for designing environmental pollution simulation models. Interactions between the three components of the environmental problem (Social, Economic and Ecological) are presented. On the light of these interactions, studies published from 2009 to 2013 are reviewed. Models are presented in terms of: model's purpose, studied variables, used data, representation of space and time, decision-making mechanism and implementation.
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