Data-based fault detection and isolation using feedback control: Output feedback and optimality

This work focuses on data-based fault detection and isolation (FDI) of nonlinear process systems. Working within the framework of controller-enhanced FDI that we recently introduced, we address and solve two unresolved, practical problems. First, we consider the case where only output measurements are available and design appropriate state estimator-based output feedback controllers to achieve controller-enhanced FDI in the closed-loop system. Precise conditions for achieving FDI using output feedback control are provided. Second, we address the problem of controller-enhanced FDI in an optimal fashion within the framework of model predictive control (MPC). We propose an MPC formulation that includes appropriate isolability constraints to achieve FDI in the closed-loop system. Throughout the manuscript, we use a nonlinear chemical process example to demonstrate the applicability and effectiveness of the proposed methods.

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