Filtragem de sistemas discretos com parametros sujeitos a saltos markovianos / Filtering of discrete-time Markov jump linear systems Markov jump linear systems




This thesis addresses the H2 and Hoo filtering design problem of discrete-time Markov jump linear systems. First, under the assumption that the Markov parameter is measurable, we provide the characterization of all filters such that the estimation errar remains bounded by a given narm leveI, yielding the complete solution of the mode-dependent filtering design problem. Based on this result, a robust filter design to deal with convex bounded parameter uncertainty is considered. In the sequeI, a design procedure for modeindependent filtering design is proposed. All filters are designed by solving linear matrix inequalities. The theory is illustrated by means of a practical example, consisting the data communication through a markovian channel.


linear matrix inequalities processos de sistemas estocasticos kalman filter markov robust filtering sistemas lineares teoria do controle discrete-time systems network filtering

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