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In this paper, for a class of linear systems with unknown parameters, a direct model reference adaptive control scheme in output feedback form has been presented, which assures stable adaptation in the presence of input saturation. Also, under certain assumptions one can guarantee that the adaptive control signal will avoid input saturation. In addition, by considering that the error model is in a...
In this paper a new Active Queue Management (AQM) strategy based on Nonlinear Generalized Minimum Variance (NGMV) control is proposed for TCP/AQM networks. The NGMV control is a model free method which only requires output of the system. Cost function of this method involves weighted output error and weighted control signal terms. The NGMV controller is implemented in order to minimize the cost function...
The objective of this paper is to model, identify, and detect and isolate faults to an industrial gas turbine. The detection scheme is based on the generation of so-called “residuals” that are errors between estimated and measured variables of the process. An ARX model is used for residual generation, while for residual evaluation a neural network classifier for MLP is used. The proposed fault detection...
In this paper a novel stochastic adaptive sliding mode observer is developed which is able to estimate the states of an uncertain chaotic system with model and parametric uncertainties. The structure of the model uncertainty is unknown but its upper bound is estimated using adaptive methods. The unknown parameters are estimated using a proposed adaptation law. In addition, the effects of noise are...
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