FRSFCO - Producción de Investigación

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Author: search.filters.author.Adam, Eduardo J.

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Now showing 1 - 10 of 21
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    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.
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    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.
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    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.
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    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.
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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.
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    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.
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    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.
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    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.
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    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.
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    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.