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International audienceThis work is dedicated to the synthesis of a new fault detection and identification scheme for the actuator and/or sensor faults modeled as unknown inputs of the system. The novelty of this scheme consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output in order to estimate the unknown inputs presented as faults. The designed observer exploits the input/output measurements to reconstruct the Laguerre filter outputs where the stability and the convergence properties are ensured by using Linear Matrix Inequality. However, a significant reduction of this model is subject to an optimal choice of both Laguerre poles which is achieved by a new proposed identification approach based on a genetic algorithm. The performances of the proposed identification approach and the resulting PIO are tested on numerical simulation and validated on a 2nd order electrical linear system

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In this paper, in order to synthesize a control law we propose a new approach that enables identification of the intermediate equilibrium points of a nonlinear system, knowing the first and the last ones. These points are those around which the nonlinear system is linearized and therefore yields local models (sub-models) that contribute to forming the multimodel describing the nonlinear system. This approach is based on the transition from a given point (source) to the next by varying a scheduling parameter (SP) defining the source point sub-model. The variation of this parameter is limited by the maximum value of the stability margin determined by the loop shaping design procedure approach (LSDP) applied to such a sub-model. Hence, the new equilibrium point is defined by the new obtained value of the SP for which the gap metric between this sub-model and the one corresponding to the new value of SP is larger than the given stability margin. The different robust controllers synthesized for the different equilibrium points will be used to synthesize the robust control of the nonlinear system, by applying the gain-scheduling technique. The proposed transition approach as well as the robust control algorithm were validated on the continuous stirred tank reactor (CSTR) system.

The novelty of this work consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output. This is in order to estimate the unknown outputs presented as faults and to detect the time instant corresponding to the system malfunction. The stability and the convergence properties of the proposed PIO are ensured by using Linear Matrix Inequality. Furthermore an optimal identification of both Laguerre poles is achieved by a genetic algorithm approach where a parametric significant reduction is ensured to guarantee a reduced observer. The performances of the identification approach and the resulting PIO are tested on an experimental 2 nd order electrical system. INDEX TERMS ARX-Laguerre model, genetic algorithm, proportional-integral observer.