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Abstract

This paper presents a data driven Koopman operator based framework for designing robust state observers for nonlinear systems. Based on a finite dimensional surrogate of the Koopman generator, identified via an extended dynamic mode decomposition procedure, a tractable formulation of the observer design is enabled on the data driven model with conic uncertainties. The resulting problem is cast as a semidefinite program with linear matrix inequalities, guaranteeing exponential convergence of the observer with a predetermined rate in a probabilistic sense. The approach bridges the gap between statistical error tolerance and observer convergence certification, and enables an explicit use of linear systems theory for state observation via a data driven linear surrogate model. Numerical studies demonstrate the effectiveness and flexibility of the proposed method.