Facultad Regional San Francisco

Permanent URI for this communityhttp://48.217.138.120/handle/20.500.12272/109

Browse

Subject: search.filters.subject.CSTR

Search Results

Now showing 1 - 6 of 6
  • Thumbnail Image
    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.
  • Thumbnail Image
    Item
    A qLPV Nonlinear Model Predictive Control with Moving Horizon Estimation
    (2021-10-15) Morato, Marcelo M.; Bernardi, Emanuel; Stojanović, Vladimir
    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 terminal ingredients, computed offline via Linear Matrix Inequalities. The online operation comprises three consecutive Quadratic Programs (QPs) and, thus, is computationally efficient and able to run in real-time for a variety of applications. These QPs stand for the control optimization (MPC) and a Moving-Horizon Estimation (MHE) scheme that predicts the behaviour of the scheduling parameters along the future horizon. The method is practical and simple to implement. Its effectiveness is assessed through a benchmark example (a CSTR system).
  • Thumbnail Image
    Item
    A Procedure for Determination of Reduced Polytopic Models Based on Robust Multi-Model Representation of Large Dimensions
    (Universidad Nacional de Misiones. Facultad de Ingeniería., 2023-11-03) Cappelletti, Carlos Alberto; Pipino, Hugo; Bernardi, Emanuel; Adam, Eduardo J.
    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 captures the reactor behavior across its operational range. To achieve this, a methodology is presented, which incorporates parameter variations as uncertainties within the model. The result is a concise polytopic representation, reduced to its vertices, that effectively captures the system dynamics and encompasses the range of parameter uncertainties.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Item
    Adaptive multi-model predictive control applied to continuous stirred tank reactor
    (2021-02-06) Pipino, Hugo; Cappelletti, Carlos Alberto; Adam, Eduardo J.
    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 in the multi-model and a terminal invariant set for all the systems that are within the system polytope. This proposed method uses a virtual model-process tuning variable, which is optimized to find the best Linear Time Invariant ( LTI ) prediction sequence for the horizon, based on the LTI vertices of the polytopic system. Finally, the proposed adaptive MPC is applied to a continuous stirred tank reac- tor (CSTR) system. Discussions are set upon the a-priori design procedure, the online computational effort and application difficulties.
  • Thumbnail Image
    Item
    Formulación de un LPV-MPC Adaptativo para Procesos Industriales No Lineales.
    (2020-10-28) Pipino, Hugo; Bernardi, Emanuel; Morato, Marcelo M.; Cappelletti, Carlos Alberto; Adam, Eduardo J.; Normey-Rico, Julio E.
    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 Control Predictivo basado en Modelos (MPC) para procesos no lineales representados a través de modelos Lineales de Parámetros Variables (LPV). El método propuesto se formula como un MPC adaptativo basado en la solución de dos problemas consecutivos de Programación Cuadrática (QP), resueltos en cada instante de muestreo. El primer QP tiene un horizonte hacia atrás y estima una variable de ajuste asociada al proceso, que se utiliza para determinar el mejor modelo lineal de predicción. El segundo QP utiliza este modelo para optimizar el desempeño a lo largo del horizonte futuro. El método propuesto se aplica a un sistema Reactor Continuo de Tanque Agitado (CSTR). Las discusiones se constituyen en torno al procedimiento de diseño a-priori, el esfuerzo computacional en línea y las dificultades de su aplicación.