Browsing by Author "Normey-Rico, Julio E."

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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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    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.
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    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.
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    Sub-optimal Linear Parameter Varying Model Predictive Control for Solar Collectors
    (IEEE, Institute of Electrical and Electronics Engineers, 2020-02-26) Morato, Marcelo M.; Pipino, Hugo; Bernardi, Emanuel; Ferreyra, Diego M.; Adam, Eduardo J.; Normey-Rico, Julio E.
    This short paper investigates the temperature control of a flat-plate water-heating solar collector. This nonlinear system is modelled via a quasi-linear parameter varying setting. To address this control problem, a model predictive control algorithm is formulated, considering a frozen guess for the evolution of the scheduling parameters, set-sequence constraints and a Lyapunov-decreasing terminal cost. The advantage of this method is that it uses standard quadratic programming problems and does not have to resort to nonlinear optimization. Through simulation, it is demonstrated that it can yield successful performances.