ListarFacultad Regional San Francisco por tema "CSTR"

Mostrando ítems 1-5 de 5

  • A Procedure for Determination of Reduced Polytopic Models Based on Robust Multi-Model Representation of Large Dimensions 

    Cappelletti, Carlos A.; Pipino, Hugo; Bernardi, Emanuel; Adam, Eduardo J.; 0009-0009-7416-6955; 0000-0003-4937-6685; 0000-0001-5248-9352; 0000-0003-0156-9832 (Universidad Nacional de Misiones. Facultad de Ingeniería., 2023-11-03)
    This study entails a meticulous examination of the dynamic characteristics exhibited by the continuous stirred tank reactor (CSTR) with the objective of establishing a robust multi-model representation that accurately ...

  • A qLPV Nonlinear Model Predictive Control with Moving Horizon Estimation 

    Morato, Marcelo M.; Bernardi, Emanuel; Stojanović, Vladimir; 0000-0002-7137-0522; 0000-0002-7137-0522; 0000-0002-6005-2086 (2021-10-15)
    This paper presents a Model Predictive Control (MPC) algorithm for Nonlinear systems represented through quasiLinear Parameter Varying (qLPV) embeddings. Input-to-state stability is ensured through parameter-dependent ...

  • Adaptive multi-model predictive control applied to continuous stirred tank reactor 

    Pipino, Hugo; Cappelletti, Carlos A.; Adam, Eduardo J.; 0000-0003-4937-6685; 0009-0009-7416-6955; 0000-0003-0156-9832 (2021-02-06)
    This paper investigates the design of a Model Predictive Control ( MPC ) formulation for the case of poly- topic multi-model system representation. An adaptive MPC is developed taking into account the schedul- ing parameters ...

  • Formulación de un LPV-MPC Adaptativo para Procesos Industriales No Lineales. 

    Pipino, Hugo; Bernardi, Emanuel; Morato, Marcelo M.; Cappelletti, Carlos A.; Adam, Eduardo J.; Normey-Rico, Julio E. (2020-10-28)
    En general los procesos de la industria química son no lineales, lo que hace que los algoritmos convencionales de control predictivo lineal resulten no factibles. Por lo tanto, este artículo investiga una formulación de ...

  • Predictive Control Methods for MultiModel Systems 

    Pipino, Hugo; Bernardi, Emanuel; Cappelletti, Carlos A.; Adam, Eduardo J.; 0000-0003-4937-6685; 0000-0001-5248-9352; 0009-0009-7416-6955; 0000-0003-0156-9832 (2020-12-04)
    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 ...