Simultaneous Parameters Identification and State Estimation based on Unknown Input Observer for a class of LPV Systems

Abstract : A novel unknown input observer structure for parameters and state estimation is proposed to enhance the performance of the estimator. In this paper, we suggest how a failed matching condition in a nonlinear unknown input observer can be recovered by using time delayed measurement to solve the inversing problem. Based on delayed outputs, an augmented system is constructed from which the parameters of the model and the system states can be simultaneously estimated. The augmented nonlinear model is transformed into a Takagi Sugeno (TS) form. Sufficient conditions for the existence of the estimator are given in terms of linear matrix inequalities (LMIs). Using the obtained information on the unknown input observer, unknown parameters are identified. Finally, the feasibility and the effectiveness of the suggested approach is demonstrated on examples.
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Majda Fouka, Lamri Nehaoua, Hichem Arioui, Saïd Mammar. Simultaneous Parameters Identification and State Estimation based on Unknown Input Observer for a class of LPV Systems. 2018 Annual American Control Conference (ACC 2018), Jun 2018, Milwaukee, United States. pp.1120--1125, ⟨10.23919/ACC.2018.8431615⟩. ⟨hal-01871107⟩

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