Facultad Regional San Francisco
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Item A Comparative Analysis of Adaptive Predictive Control Methods Applied in a Heat Exchanger(Universidad Nacional de Misiones. Facultad de Ingeniería., 2023-11-03) Pipino, Hugo; Adam, Eduardo J.Most industrial processes are nonlinear, which complicates the application of conventional Model-based Predictive Control (MPC) algorithms. Consequently, in this article, the formulations of MPC methods for nonlinear processes represented through polytopic Linear Parameter-Varying models are analysed. The compared methods are adaptive algorithm, synthesised with a prediction model based on a scheduling polytope. At each discrete sampling instant, they determine a model, used for prediction purposes; and optimise the process performances over a finite prediction horizon. These methods are applied to control of a Heat Exchanger system, from which the performance and effectiveness of each technique are discussed. The simulation results are thoroughly analyzed, and the advantages and disadvantages of each strategy are discussed.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.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.Item Adaptive Predictive Control for Industrial Processes(IEEE, 2021-11-05) Bernardi, Emanuel; Pipino, Hugo; Cappelletti, Carlos Alberto; Adam, Eduardo J.In this work a predictive controller formulation is developed within a linear parameter-varying formalism, which serves as a non-linear process model. The proposed strategy is an adaptive Model-based Predictive Controller (MPC), designed with terminal set constraints and considering the scheduling polytope of the model. At each sample time, two Quadratic Programming (QP) problems are solved: the first QP considers a backward horizon to find a virtual model-process tuning variable that defines the best LTI prediction model, considering the vertices of the polytopic system; then, the second QP uses this LTI model to optimise performances along a forward horizon. This paper ends with a realistic solar thermal collector process simulation, comparing the proposed MPC to other techniques from the literature. Discussions regarding the results, the design procedure and the computational effort are presented.Item Control predictivo basado en modelo con desigualdades matriciales aplicado a la industria de procesos(Universidad Nacional del Litoral (UNL), 2023-06-09) Pipino, Hugo; Adam, Eduardo J.Los sistemas industriales modernos, basados en proveer una mejor calidad y uniformidad de sus productos aprovechando mejor los recursos disponibles y favoreciendo el cuidado del medioambiente, incorporan sistemas de control cada vez más complejos. La industria de procesos químicos tiene un gran y continuo desarrollo que ha sido acompañado de avances en problemas de computación, control y optimización. Entre ellos, las técnicas de control avanzadas se fueron estableciendo para mejorar el desempeño y garantizar la estabilidad del sistema controlado. En consecuencia, los controladores basados en problemas de optimización se implementan en una amplia gama de aplicaciones industriales. Los mismos toman en cuenta los objetivos requeridos e incorporan las restricciones operativas del sistema. Así, el control predictivo basado en modelos utiliza un modelo de predicción para obtener las respuestas futuras y aplicar aquella que mejor satisfaga los objetivos propuestos. Por lo tanto, para diseñar estos esquemas de control, se deben tener en cuenta varios aspectos, los objetivos requeridos, el modelo de la planta, las restricciones impuestas, la ley de control, el tamaño del horizonte de predicción, entre otros. Tomando en cuenta estos aspectos, esta tesis aborda el diseño, desarrollo y evaluación de estrategias de control predictivo basado en modelos aplicado a procesos típicos de la industria de procesos, que aseguren estabilidad del sistema controlado, cumplimiento de las restricciones y que contemplen incertidumbre en el modelo de predicción, ya sea por las que surgen de la naturaleza no lineal del sistema o porque no se conocen con exactitud los parámetros del modelo.Item Diseño de Observadores para la Detección y Diagnóstico de Fallas Aplicados a la Industria de Procesos(UTN, 2020-05-20) Bernardi, Emanuel; Adam, Eduardo J.En las últimas décadas, los sistemas de control han evolucionado, desde simples estructuras de realimentación mecánicas hasta la concepción de estrategias avanzadas, cuya implementación sobre dispositivos electrónicos permite significativas mejoras en la operación de sistemas complejos y/o altamente inestables, optimizando costos y esfuerzos de control. A menudo, escenarios inesperados, o eventos no contemplados previamente en el diseño del controlador, suceden en los sistemas provocando rendimientos insatisfactorios o incluso comprometiendo su estabilidad. En general, tales eventos se generan ante el mal funcionamiento de los componentes del sistema (actuadores, sensores, entre otros). Por lo que para afrontar estos inconvenientes resulta estrictamente necesario el desarrollo de sistemas de detección, aislación y diagnóstico de fallas capaces de inferir, en una fase temprana de su desarrollo, la ocurrencia de una falla e indicar la razón que la causó, conociendo en detalle las anomalías del sistema, de forma tal, que permita a los controladores tomar decisiones adecuadas, mientras se mantienen las características de rendimiento y estabilidad deseadas. Es por ello, que a través de este trabajo de tesis se busca conocer, evaluar y desarrollar técnicas de detección y diagnóstico de fallas con fines prácticos. Específicamente, se presenta el estado actual de conocimiento sobre el tema acompañado por los principales conceptos básicos involucrados, para luego abordar aquellas herramientas que emplean redundancia analítica generada a partir del conocimiento del modelo matemático del proceso. Esto es, profundizar el abordaje de los esquemas de detección y diagnóstico de fallas basados en observadores de estados tanto sobre sistemas lineales e invariantes en el tiempo, como para sistemas lineales de parámetros variables. Los esquemas de detección y diagnóstico propuestos se evaluaron, mediante simulaciones numéricas, sobre tres operaciones unitarias típicas de la industria química de procesos. Siendo estas: el proceso de dos tanques sin interacción, un sistema intercambiador de calor y un reactor continuo de tanque agitado.Item Esquema de detección y diagnóstico de fallas basado en observadores de tiempo discreto(Secyt UTN Facultad Regional San Francisco, 2019-10) Bernardi, Emanuel; Adam, Eduardo J.En la actualidad los sistemas de control están presentes en casi todos los aspectos de nuestras vidas, su implementación va desde las formas más sencillas en elementos del hogar, hasta las aplicaciones más complejas en aviónica, industrias químicas, petroquímicas y nucleares (Gertler, 1998). El diseño de un controlador convencional, ante un evento como el mal funcionamiento de un actuador, sensor u otro componente del sistema, puede resultar en un rendimiento insatisfactorio, o incluso llevar al sistema a la inestabilidad. En base a lo previamente expuesto, el presente artículo presenta el diseño de un conjunto de observadores de orden reducido para el desarrollo de un esquema FDD de tiempo discreto, capaz de detectar, aislar y diagnosticar el funcionamiento defectuoso de actuadores en sistemas no lineales, que aceptan representación Lineal de Parámetros Variables (LPV), para luego evaluar su comportamiento y capacidades sobre un proceso típico de la industria de procesos químicos.Item Fault-tolerant energy management for an industrial microgrid: A compact optimization method(2021-01-10) Bernardi, Emanuel; Morato, Marcelo M.; Costa Mendes, Paulo R.; Adam, Eduardo J.; Normey-Rico, Julio E.This work presents an optimization-based control method for the fault-tolerant energy management task of an industrial energy microgrid, based on a sugarcane power plant. The studied microgrid has several renewable energy sources, such as photovoltaic panels, wind turbines and biomass power generation, being subject to different operational constraints and load demands. The proposed management policy guarantees that these demands are met at every sampling instant, despite eventual faults. This law is derived from the solution of an optimization problem that combines the formalism of a Moving Horizon Estimation (MHE) scheme (to estimate faults) and a Model Predictive Control (MPC) loop (for fault-tolerant control goals); it chooses which energy source to use, seeking maximal profit and increased sustainability. The predictive controller part of the scheme is based on a linear time-varying model of the process, which is scheduled with respect to the fault estimation brought up by the MHE. Via numerical simulations, it is demonstrated that the proposed method, when com- pared to other MPC strategies, exhibits enhanced performances.Item Fault-tolerant Model Predictive Control Strategy Applied to Industrial Processes(IEEE, 2019-09-20) Bernardi, Emanuel; Cappelletti, Carlos Alberto; Adam, Eduardo J.This paper presents a strategy to address the income of actuators faults using a model-based predictive controller scheme, which allows to track a reference even in the presence of actuator faults. The proposed fault-tolerant control system adopts a model predictive control technique to design a reconfigurable fault-tolerant controller and a reduce-order observer to achieve the fault detection and diagnosis function. Simulation results, based on two typical chemical industries processes, are given to illustrate the use and performance of such approach.Item Fault-tolerant Model-based Predictive Control Applied to Industrial Processes(UNL, 2021-10-21) Bernardi, Emanuel; Adam, Eduardo J.Modern plants rely on sophisticated control systems to meet performance and stability requirements. In particular, a conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. In view of these aspects, this thesis addresses the design, development and evaluation of fault-tolerant controllers for typical industrial processes, which ensure the compliance of operational constraints despite the presence of faults. To begin with, the current state-of-art and the main specific concepts are introduced. Then, two model-based strategies are presented. On the one side, the design of a novel observer-based fault detection and diagnosis scheme and the development of an adaptive predictive controller are combined to deploy a non-linear active fault-tolerant control system, on the basis of the linear parameter varying system representation. This proposed scheme is evaluated on typical non-linear chemical industrial processes. On the other hand, an optimisation-based fault-tolerant predictive controller was proposed to develop a tertiary-level energy management system, based on a sugarcane distillery power plant. Lastly, it is important to remark that for each proposed scheme a realistic simulation scenario was presented. Enabling vast discussions about its performance and effectiveness, via graphical observations and metric indices.Item Fault-tolerant predictive control based on linear parameter varying scheme for industrial processes(2021-12-01) Bernardi, Emanuel; Adam, Eduardo J.Background: Process safety is a major concern in the researchers community, both in the past and today. However, the hardware complexity and the involved non-linear dynamics of industrial processes could lead to unsatisfactory behavior of traditional control methods. Methods: To cope with these issues, this paper presents a model-based strategy for fault tolerance in non-lin- ear chemical processes. Specifically, an observer-based fault detection and diagnosis scheme was imple- mented, which generates early and detailed fault information. Therefore, this valuable data was used to compensate the effects induced by actuator and sensor faults throughout the use of an integrated optimiza- tion-based identification and model predictive control technique, which allowed to track a reference even in the presence of faults. Significant Findings: This method reinforces the inherent robustness against faults of linear parameter varying predictive controllers. Moreover, the observers convergence and the controller stability were guaranteed in terms of linear matrix inequalities problems. A simulation based on a typical chemical industrial process, the highly non-linear continuous stirred tank reactor shows that the proposed method can achieve satisfactory performance in fault tolerance.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.Item 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.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 Monitoreo de catástrofes naturales a partir de la Obtención y Procesamiento de Imágenes satelitales(2020-06-19) Olmedo, Paula Beatriz; Miretti, Marco; Bernardi, Emanuel; Redolfi, Javier; Peretti, Gastón Carlos; Adam, Eduardo J.En la actualidad, el desarrollo de la industria aeroespacial a la par del desarrollo tecnológico, facilita el acceso a la información brindada por los satélites. En este contexto, el trabajo que a continuación se describe tiene por objetivo captar señales satelitales, especialmente imágenes, con el fin de almacenarlas y procesarlas en tiempo real para obtener modelos que auxilien en el análisis y prevención de emergencias ambientales. Es de destacar que este trabajo forma parte de un proyecto de mayor alcance, cuya primera etapa consistió en la construcción de una estación terrena para la adquisición de las imágenes de interés.Item MPC for linear systems with parametric uncertainty(IEEE, 2019-09-20) Pipino, Hugo; Adam, Eduardo J.This paper deals with linear systems with paramet- ric uncertainty using a model-based predictive control (MPC). When the uncertainty of the system is significant, the MPC performance can be deteriorated or even the optimization problem can be unfeasible. In this paper, a MPC for linear systems with parametric uncertainty is presented. This controller considers the weight variable of a linear parameter-varying (LPV) system as a decision variable of the optimization problem and a terminal invariant set for all the systems that are within the uncertainty polytope. Finally, this controller is applied to a mass-spring-damper system to verify its properties.Item Nonlinear Fault-tolerant Model Predictive Control Strategy for Industrial Processes(IEEE, 2020-10-28) Bernardi, Emanuel; Adam, Eduardo J.This short paper presents a strategy to tolerate the income of additive faults in nonlinear chemical processes. For that, an observed-based fault detection and diagnosis scheme is implemented to generate an early and detailed fault information. Then, this valuable knowledge is used to compensate the effects induced by actuators and sensors faults throughout the use of an integrated optimization-based estimation and model predictive control scheme, which allows to track a reference even in presence of faults. A simulation based on a typical chemical industrial process, the highly non-linear continuous stirred tank reactor, is addressed to illustrate the design process and the performance of such approach.Item Nonlinear temperature regulation of solar collectors with a fast adaptive polytopic LPV MPC formulation(2020-09-11) Pipino, Hugo; Morato, Marcelo M.; Bernardi, Emanuel; Adam, Eduardo J.; Normey-Rico, Julio E.Temperature control in solar collectors is a nonlinear problem: the dynamics of temperature rise vary according to the fluid flowing through the collector and to the temperature gradient along the collector area. In this way, this work investigates the formulation of a Model Predictive Control (MPC) application developed within a Linear Parameter Varying (LPV) formalism, which serves as a model of the solar collector process. The proposed system is an adaptive MPC, developed with terminal set constraints and considering the scheduling polytope of the model. At each instant, two Quadratic Programming (QPs) programs are solved: the first considers a backward horizon of N steps to find a virtual model-process tuning variable that defines the best LTI prediction model, considering the vertices of the polytopic system; then, the second QP uses this LTI model to optimize performances along a forward horizon of N steps. The paper ends with a realistic solar collector simulation results, comparing the proposed MPC to other techniques from the literature (linear MPC and robust tube-MPC). Discussions regarding the results, the design procedure and the computational effort for the three methods are presented. It is shown how the proposed MPC design is able to outrank these other standard methods in terms of reference tracking and disturbance rejection.Item Observador de salida de tiempo discreto para detección y diagnóstico de fallas en elementos sensores(Secyt UTN Facultad Regional San Francisco, 2020-10) Bernardi, Emanuel; Adam, Eduardo J.En las últimas décadas, los sistemas de control han evolucionado, desde simples estructuras de realimentación mecánicas hasta la concepción de estrategias avanzadas, cuya implementación sobre dispositivos electrónicos permite significativas mejoras en la operación de sistemas complejos y/o altamente inestables, optimizando costos y esfuerzos de control. El diseño de un controlador convencional, ante un evento como el mal funcionamiento de un actuador, sensor u otro componente del sistema, puede resultar en un rendimiento insatisfactorio, o incluso llevar al sistema a la inestabilidad. Para superar estas debilidades, resulta necesario el desarrollo de sistemas de Detección y Diagnóstico de Fallas (FDD) capaces de inferir, en una fase temprana de su desarrollo, la ocurrencia de una falla e indicar la razón que la causó, conociendo en detalle las anomalías del sistema. En base a lo previamente expuesto, el presente trabajo presenta el diseño de un conjunto de Observadores de Salida con Entrada Desconocida (LPV-UIOO) para el desarrollo de un esquema FDD de tiempo discreto, capaz de detectar, aislar y diagnosticar el funcionamiento defectuoso de elementos sensores en sistemas no lineales, que aceptan representación Lineal de Parámetros Variables (LPV), para luego evaluar su comportamiento y capacidades sobre un proceso típico de la industria de procesos químicos.Item Observer-based fault detection and diagnosis strategy for industrial processes(2020-07-30) Bernardi, Emanuel; Adam, Eduardo J.This study presents the design of a fault detection and diagnosis (FDD) scheme, composed from a bank of two types of observers, applied to linear parameter varying (LPV) systems. The first one uses a combination of reduced-order LPV observers to detect, isolate and estimate actuators faults, and the second one consists of a set of full-order LPV unknown input observers (UIO) to detect, isolate and estimate sensors faults. The observers’ design, convergence and its stability conditions are guaranteed in terms of linear matrix inequalities (LMI). Therefore, the main purpose of this work is to provide a novelty model-based observers’ technique to detect and diagnose faults upon non-linear systems. Simulation results, based on two typical chemical industrial processes, are given to illustrate and discuss the implementation and performance of such an approach.