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A sliding mode observer in the presence of sampled output information and its application to robust fault reconstruction is studied.
Then, we consider that we don't know the derivative of the roll required by the controller and we show how to construct a sliding mode observer in presence of the controller.
After an analysis which demonstrates the fact that it is impossible to use only a high order sliding mode observer, in the case of sparse measurement, it is shown that a high order sliding mode observer coupled with an impulsive observer is a pertinent solution at least for some particular class of systems.
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Therefore, both developed observer approaches avoid the sliding surface switching problem of traditional sliding mode observers in application to switched systems.
The results obtained through numerical simulation show a good behaviour of the sliding-mode observer in the presence of the parameter's uncertainties that interfere with the process.
In this work, we proposed a novel hybrid fuzzy-sliding mode observer designed in such a manner that it can be utilized to estimate various parameters by simply using the related process model, without redesigning the structure of the whole observer.
Besides, the presented method has advantages over the extended Kalman filter (EKF) and sliding mode observer (SMO) algorithms in terms of computation cost, estimation accuracy and convergence rate.
The sliding mode observer method was explained in detail in the literature [29, 30, 31, 32].
A nonlinear sliding mode observer has been designed in this paper for estimating the temperature distribution in a hydrogen fed SOFC stack.
A sliding mode observer is derived such that the observation errors converge to zero asymptotically in fInite time.
Second, a step by step sliding mode observer for fault detection and estimation in nonlinear fractional-order systems is proposed.
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