6533b872fe1ef96bd12d3897

RESEARCH PRODUCT

Design of unknown inputs proportional integral observers for TS fuzzy models

Hamid Reza KarimiT. YoussefMohammed ChadliMimoun Zelmat

subject

Lyapunov functionUnknown inputs reconstructionCognitive NeuroscienceLinear matrix inequalityComputer Science Applications1707 Computer Vision and Pattern RecognitionFuzzy logicComputer Science ApplicationsStability conditionssymbols.namesakeDecision variablesComputer Science::Systems and ControlArtificial IntelligenceControl theoryBounded functionNorm (mathematics)Unmeasurable decision variablessymbolsTS fuzzy modelsProportional integral observer; TS fuzzy models; Unknown inputs reconstruction; Unmeasurable decision variables; Artificial Intelligence; Computer Science Applications1707 Computer Vision and Pattern Recognition; Cognitive NeuroscienceProportional integral observerMathematics

description

In this paper the design of unknown inputs proportional integral observers for Takagi-Sugeno (TS) fuzzy models subject to unmeasurable decision variables is proposed. These unknown inputs affect both state and output of the system. The synthesis of these observers is based on two hypotheses that the unknown inputs are under the polynomials form with their kth derivatives zero for the first one and bounded norm for the second one, hence two approaches. The Lyapunov theory and L"2-gain technique are used to develop the stability conditions of such observers in LMIs (linear matrix inequality) formulation. A simulation example is given to validate and compare the proposed design conditions for these two approaches.

yearjournalcountryeditionlanguage
2014-01-01Neurocomputing
https://doi.org/10.1016/j.neucom.2013.06.024