Predicción para control /

Tese (Doutorado) - Universidad de Sevilla, Escuela Superior de Ingenieros

Optimal control analysis and Practical NMPC applied to refrigeration systems

This work is focused on optimal control of mechanical compression refrigeration systems. A reduced-order state-space model based on the moving boundary approach is proposed for the canonical cycle, which eases the controller design. The optimal cycle (that satisfying the cooling demand while maximizing efficiency) is defined by three variables, but only two inputs are available, therefore the controllability of the proposed model is studied. It is shown through optimization simulations how optimal cycles for a range of the cooling demand turn out not to be achieved by keeping the degree of superheating to a minimum. The Practical NMPC and a well-known feedback-plus-feedforward strategy from the literature are compared in simulation, both showing trouble in reaching the optimal cycle, which agrees with the controllability study.Es la versión aceptada del artículo. Se puede consultar la versión final en https://doi.org/10.1016/j.isatra.2020.07.041Ministerio de Ciencia e InnovaciónBrazilian CNP

Smith Predictor with Inverted Decoupling for Square Multivariable Time Delay Systems

Versión del autorThis paper presents a new methodology to design multivariable Smith predictor for n×n processes with multiple time delays based on the centralized inverted decoupling structure. The controller elements are calculated in order to achieve good reference tracking and decoupling response. Independently of the system size, very simple general expressions for the controller elements are obtained. The realizability conditions are provided and the particular case of processes with all of its elements as first order plus time delay systems is discussed in more detail. A diagonal filter is added to the proposed control structure in order to improve the disturbance rejection without modifying the nominal set-point response and to obtain a stable output prediction in unstable plants. The effectiveness of the method is illustrated through different simulation examples in comparison with other works

Efficient simulation strategy for PCM-based cold-energy storage systems

This paper proposes a computationally efficient simulation strategy for cold thermal energy storage (TES) systems based on phase change material (PCM). Taking as a starting point the recent design of a TES system based on PCM, designed to complement a vapour-compression refrigeration plant, the new highly efficient modelling strategy is described and its performance is compared against the pre-existing one. The need for a new computationally efficient approach comes from the fact that, in the near future, such a TES model is intended to be used in combination with the model of the own mother refrigeration plant, in order to address efficient, long-term energy management strategies, where computation time will become a major issue. Comparative simulations show that the proposed computationally efficient strategy reduces the simulation time to a small fraction of the original figure (from around 1/30th till around 1/120th, depending on the particular choice of the main sampling interval), at the expense of affordable inaccuracy in terms of the PCM charge ratio.Ministerio de Ciencia e Innovació

The COVID-19 (SARS-CoV-2) uncertainty tripod in Brazil : assessments on model-based predictions with large under-reporting

The COVID-19 pandemic (SARS-CoV-2 virus) is the global crisis of our time. The absence of mass testing and the relevant presence of asymptomatic individuals causes the available data of the COVID-19 pandemic in Brazil to be largely under-reported regarding the number of infected individuals and deaths. We develop an adapted Susceptible-Infected-Recovered (SIR) model, which explicitly incorporates the under-reporting and the response of the population to public health policies (confinement measures, widespread use of masks, etc). Large amounts of uncertainty could provide misleading predictions of the COVID-19 spread. In this paper, we discuss the role of uncertainty in these model-based predictions, which is illustrated regarding three key aspects: (i) Assuming that the number of infected individuals is under-reported, we demonstrate anticipation regarding the infection peak. Furthermore, while a model with a single class of infected individuals yields forecasts with increased peaks, a model that considers both symptomatic and asymptomatic infected individuals suggests a decrease of the peak of symptomatic cases. (ii) Considering that the actual amount of deaths is larger than what is being registered, we demonstrate an increase of the mortality rates. (iii) When we consider generally under-reported data, we demonstrate how the transmission and recovery rate model parameters change qualitatively and quantitatively. We also investigate the “the uncertainty tripod”: under-reporting level in terms of cases, deaths, and the true mortality rate of the disease. We demonstrate that if two of these factors are known, the remainder can be inferred, as long as proportions are kept constant. The proposed approach allows one to determine the margins of uncertainty by assessments on the observed and true mortality rates

Fast constrained dynamic matrix control algorithm with online optimization

[EN] This work proposes a predictive control technique to be applied in fast processes using online optimization. Currently, advanced controllers are increasingly needed in industry at low levels of automation, which are associated with sampling times on the order of milliseconds or microseconds. The quadratic programming resulting from the control problem of a predictive control algorithm with constraints, such as Dynamic Matrix Control (DMC), which is one of the most used alternatives in industry, can be considered computationally expensive and becomes a limitation to embed and use the DMC in plants with fast sample rates. This paper proposes a solution to this problem based on the dual accelerated gradient projection method, which has shorter convergence times than other solutions in the literature based on predictive control strategies with online optimization. The proposed approach was tested in simulation to control a semiactive automotive suspension system, which has fast dynamics, showing that satisfactory results can be obtained with a sampling time of 5 ms. Moreover, the proposed control was implemented in a field-programmable gate array (FPGA) and the resulting quadratic programming problem was calculated in microseconds, which allows the use of the DMC to control very fast processes.[ES] Este trabajo propone una técnica de control predictivo para ser aplicada en procesos rápidos utilizando optimización en línea. Actualmente, en el sector industrial, los controladores avanzados son cada vez más necesarios en los bajos niveles de automatización, que están asociados con tiempos de muestreo del orden de milisegundos o microsegundos. La programación cuadrática resultante del problema de control de un algoritmo de control predictivo con restricciones, como por ejemplo el control por matriz dinámica (en inglés Dynamic Matrix Control - DMC), que es uno de los más usados en la industria, se puede considerar computacionalmente costosa y se convierte en una limitación para empotrar y usar el DMC en plantas con tasas de muestreo rápidas. Este artículo propone una solución a este problema basada en el método de proyección de gradiente acelerada dual, que tiene tiempos de convergencia menores que otras soluciones de la literatura basadas en estrategias de control predictivo con optimización en línea. Además, el control propuesto se implementó en una matriz de puertos programables (en inglés field-programmable gate array FPGA) y el problema de programación cuadrática resultante se calculó en microsegundos, lo que permite el uso del DMC en procesos muy rápidos.Los autores agradecen el apoyo financiero dado por CNPq – proyectos 304032/2019-0 y 315546/2021-2.Peccin, VB.; Lima, DM.; Flesch, RCC.; Normey-Rico, JE. (2022). Control por matriz dinámica rápido utilizando optimización en línea. Revista Iberoamericana de Automática e Informática industrial. 19(3):330-342. https://doi.org/10.4995/riai.2022.16619OJS33034219