The problem of observer-based robust fault reconstruction for a class of nonlinear sampled-data systems is investigated. A discrete time\nLipschitz nonlinear system is first established, and its Euler-approximate model is described; then, an Euler-approximate\nproportional integral observer (EPIO) is constructed such that simultaneous reconstruction of system states and actuator faults\nare guaranteed. The presented EPIO possesses the disturbance-decoupling ability because its architecture is similar to that of a\nnonlinear unknown input observer. The robust stability of the EPIO and convergence of fault-reconstructing errors are proved\nusing Lyapunov stability theory together with H? techniques. The design of the EPIO is reformulated into convex optimization\nproblem involving linear matrix inequalities (LMIs) such that its gain matrices can be conveniently calculated using standard LMI\ntools. In addition, to guarantee the implementation of the EPIO on the exact model, sufficient conditions of its semiglobal practical\nconvergence are provided explicitly. Finally, a single-link flexible robot is employed to verify the effectiveness of the proposed faultre constructing\nmethod.
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