FRSFCO - Producción de Investigación
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Item 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.Item Modeling of a Continuous Stirred Tank Reactor and Controller Design Using LMI Approaches(Wiley, 2025-01-07) Cappelletti, Carlos Alberto; Pipino, Hugo; Bernardi, Emanuel; Adam, Eduardo J.The design of non-linear control systems remains a challenge today, therefore through this work a procedure to obtain a vertex-reduced multi-model representation, without loss of convexity, is proposed as a suitable solution. That is, a novel approach which considers all parameter variations around the Continuous Stirred Tank Reactor (CSTR) system operating region is developed, resulting in a unique polytopic representation. After that, based on the linear matrix inequalities approach, a control scheme is developed to compute the optimal matrix gains, while the operating, states and inputs, constraints are satisfied and the stability conditions are ensured. Finally, the realistic simulation results highlight the model representation effectiveness in capturing the CSTR dynamic behavior in the operating region, despite parameter variations, allowing the optimal control law design, overcoming the non-linear system nature, to achieve the desired closed-loop system performance.Item Predictive Control Methods for MultiModel Systems(2020-12-04) Pipino, Hugo; Bernardi, Emanuel; Cappelletti, Carlos Alberto; Adam, Eduardo J.This paper explores the design of three different approaches of robust predictive control formulations for the case of multi-model system representations. The first one is an optimum multi-objective regulator with variable gain matrix that considers a continuous time multi-model system representation and an infinite horizon; the second one is a sub-optimal linear parameter varying model predictive controller based on a discrete time multi-model system representation with finite horizon and a sequence of contractive terminal set constraint; and, at last, an adaptive model predictive controller that considers a discrete time multi-model system representation, with finite horizon and a terminal invariant set, in common to all models within the system’s polytope. Finally, these proposed methods are applied to a continuously-stirred tank reactor (CSTR) system, whose dynamic characteristics are well known and strongly non-linear. Through the simulation results, discussions are established on the design procedure, the online computational effort, the performance indexes and the application difficulties.