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Author: search.filters.author.Adam, Eduardo J.Subject: search.filters.subject.Linear matrix inequalities

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    Adaptive model-based predictive control with changing operation points
    (Elsevier, 2025-05-28) Pipino, Hugo; Adam, Eduardo J.
    Most industrial processes are nonlinear and experience frequent variations in the operating point, which can make them impractical for real-time Model-based Predictive Control (MPC) implementation. This research explores the design and analysis of MPC formulations developed within the context of Linear Parameter Varying (LPV) model framework. These methods take into account the scheduling parameters of the multi-model and perform online process-model adaptation, obtaining a linear prediction model that allows representing the nonlinear process at each instant. Additionally, necessary conditions are established to guarantee the asymptotic stability of the feasible equilibrium set for all models contained in the LPV model. This enables the consideration of changes in operating points that occur during the normal operation of the process. The article concludes with realistic simulation results of two typical unit operations in the process industry, comparing the analyzed MPC techniques with a linear MPC present in the literature. Discussions are presented on the results in terms of performance, effectiveness, computational effort and disturbance rejection, in the presence of changing operating points.
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    Full-order Output Observer Applied to a Linear Parameter Varying System with Unknown Input
    (IEEE, 2020-12-01) Bernardi, Emanuel; Pipino, Hugo; Adam, Eduardo J.
    This short work presents the outline of a set of full-order observers, applied to linear parameter varying systems with unknown input. In particular, this approach is used to constructs a strategy to detect and diagnose sensor faults on industrial processes. The observers’ design and its stability conditions are guaranteed in terms of a linear matrix inequalities framework. As a consequence, the main purpose of this paper is to provide a model-based observers’ technique, to detect, isolate and diagnose sensor faults upon non-linear systems. At last, two numerical simulations of typical chemical industrial processes are given to illustrate its implementation and performance.