Implementation of min–max MPC using hinging hyperplanes. Application to a heat exchanger

Min–max model predictive control (MMMPC) is one of the few control techniques able to cope with modelling errors or uncertainties in an explicit manner. The implementation of MMMPC suffers a large computational burden due to the numerical min–max problem that has to be solved at every sampling time. This fact severely limits the range of processes to which this control structure can be applied. An implementation scheme based on hinging hyperplanes that overcome these problems is presented here. Experimental results obtained when applying the controller to the heat exchanger of a pilot plant are given.Ministerio de Ciencia y Tecnología DPI2001-2380-C02-01Ministerio de Ciencia y Tecnología DPI2002-04375-C03-0

Min-Max Predictive Control of a Five-Phase Induction Machine

In this paper, a fuzzy-logic based operator is used instead of a traditional cost function for the predictive stator current control of a five-phase induction machine (IM). The min-max operator is explored for the first time as an alternative to the traditional loss function. With this proposal, the selection of voltage vectors does not need weighting factors that are normally used within the loss function and require a cumbersome procedure to tune. In order to cope with conflicting criteria, the proposal uses a decision function that compares predicted errors in the torque producing subspace and in the x-y subspace. Simulations and experimental results are provided, showing how the proposal compares with the traditional method of fixed tuning for predictive stator current control.Ministerio de Economía y Competitividad DPI 2016-76493-C3-1-R y 2014/425Unión Europea DPI 2016-76493-C3-1-R y 2014/425Universidad de Sevilla DPI 2016-76493-C3-1-R y 2014/42

Adaptive Cost Function FCSMPC for 6-Phase IMs

In this paper, an adaptive cost function FCSMPC is derived from newly obtained results concerning the distribution of figures of merits used for the assessment of stator current model-based control of multi-phase induction machines. A parameter analysis of FCSMPC is carried out for the case of a six-phase motor. After extensive simulation and Pareto screening, a new structure has been discovered linking several figures of merit. This structure provides an simple explanation for previously reported results concerning the difficulty of cost function tuning for FCSMPC. In addition, the newly discovered link among figures of merit provides valuable insight that can be used for control design. As an application, a new cost function design scheme is derived and tested. This new method avoids the usual and cumbersome procedure of testing many different controller parameters.Unión Europea RTI2018-101897-B-I00Ministerio de Ciencia e Innovación RTI2018-101897-B-I00Agencia Estatal de Investigación RTI2018-101897-B-I0

Robust control of the distributed solar collector field ACUREX using MPC for tracking

17th IFAC World Congress 2008. Seoul (Korea). 06/07/2008This paper presents the application of a robust model predictive control for tracking of piece-wise constant references (RMPCT) to a distributed collector field, ACUREX, at the solar power plant of PSA (Solar Plant of Almería). The main characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong disturbances in the process. The real plant is assumed to be modeled as a linear system with additive bounded uncertainties on the states. Under mild assumptions, the proposed RMPCT can steer the uncertain system in an admissible evolution to any admissible steady state, that is, under any change of the set point. This allows us to reject constant disturbances compensating the effect of then changing the setpoint

Introducing Linear Matrix Inequalities in a Control Course

7TH IFAC SYMPOSIUM ON ADVANCES IN CONTROL EDUCATION 21/06/2006 MadridThe important role that linear matrix inequalities have attained in the last years makes it compulsory to include them in the education of a control engineer. As the development of efficient semidefinite programming algorithms date from the early nineties, there is a lack of teaching experience in this field (at least when it is compared with other well established aspects of control theory). This paper proposes a simple way to introduce linear matrix inequalities in a control course. The main objective of the paper is to show that in the formulation of (robust) control problems as linear matrix inequalities a very reduced number of elementary technical results are required. It is illustrated how to introduce, in a progressive way, these technical results along with motivating examples. All of this is done in such a way that it facilitates the assimilation of this important subject. The presented methodology has been successfully applied for more than four years in a doctoral course on control theory

Problem based learning case in a control undergraduate subject

Brescia, 4-6 Nov., 2015Student motivation is a relevant subject in many engineering areas including control and related subjects such as instrumentation. The classic approach to teaching has revealed itself a major obstacle for today’s students characterized by a being users of advanced technology in their daily life. Theoretical classes and even old laboratory experiments are of no appeal to them. In this paper a case study of the application of Problem Based Learning ideas is presented. The students are faced with a design problem in which they build a temperature control from basics components. The experience took place at the Engineering College of the University of Seville. The results shown are drawn from a three-year study and are compared with other undergraduate subjects

Identificación de las principales tipologías de nubes utilizando HOG

[Resumen] Este artículo aborda la clasificación automática de nubes a partir de imágenes tomadas desde el suelo mediante técnicas que utilizan histogramas de vectores gradiente. La identificación de nubes tiene una importancia creciente en la industria solar como medio para producir predicciones locales de nubosidad a muy corto plazo. En este contexto se presentan los histogramas de vectores gradiente para extracción de características útiles para la clasificación. Se discuten y exponen ciertas propiedades de este tipo de características que son posteriormente puestas a prueba en la clasificación de nubes. El clasificador empleado es una máquina de vectores soporte SVM. Se consideran en la clasificación las principales tipologías de nubes: cúmulos, cirros y estratos.https://doi.org/10.17979/spudc.978849749808

Selección de variables en la predicción de llamadas en un centro de atención telefónica

En este artículo se ilustra la importancia de la selección de variables independientes para modelos neuronales destinados a la predicción de la demanda en un centro de atención telefónica. Los modelos tienen como objetivo ayudar en la planificación semanal del personal del centro, tarea que se realiza con 14 días de antelación. Los modelos requeridos pueden hacer uso de gran cantidad de variables independientes. Sin embargo, el número de casos que pueden ser usados para obtener los parámetros del modelo es escaso debido a los cambios socio-económicos. Esto plantea la necesidad de seleccionar cuidadosamente las variables independientes y utilizar el menor número posible de ellas, de otro modo la generalización del modelo se degradaría. Para resolver el problema se utiliza un método mixto que permite trabajar con un alto número de variables candidatas, en una primera fase, y seleccionar más cuidadosamente un número menor de variables en una segunda fase. Los resultados obtenidos por los modelos resultantes de aplicar el método propuesto y sus variantes son analizados utilizando datos reales de un centro de atención telefónica. Los resultados de la comparación muestran que la correcta selección de variables independientes es vital para este tipo de aplicación.Unión Europea HYCON FP6-511368

Constraint Satisfaction in Current Control of a Five-Phase Drive with Locally Tuned Predictive Controllers

The problem of control of stator currents in multi-phase induction machines has recently been tackled by direct digital model predictive control. Although these predictive controllers can directly incorporate constraints, most reported applications for stator current control of drives do no use this possibility, being the usual practice tuning the controller to achieve the particular compromise solution. The proposal of this paper is to change the form of the tuning problem of predictive controllers so that constraints are explicitly taken into account. This is done by considering multiple controllers that are locally optimal. To illustrate the method, a five-phase drive is considered and the problem of minimizing x- y losses while simultaneously maintaining the switching frequency and current tracking error below some limits is tackled. The experiments showed that the constraint feasibility problem has, in general, no solution for standard predictive control, whereas the proposed scheme provides good tracking performance without violating constraints in switching frequency and at the same time reducing parasitic currents of x- y subspaces.Ministerio de Ciencia, Innovación y Universidades de España RTI2018-101897-B-I0

Rotor Current Estimation in Predictive Control of Multi-phase Drives

[EN] Predictive control of stator currents needs estimating rotor currents since they are usually not measured. Rotor currents influence the future evolution of the stator currents, affecting the predictions needed by the predictive controller. The usual procedure is to estimate the effect of rotor currents on stator current evolution. This is done by adding the prediction error for the past sampling to the actual prediction. In this paper a new estimation method is proposed using the equivalent circuit of the induction machine. Simulation and experimental results are provided using a multi-phase induction machine as a specially demanding benchmark, showing the superiority of the proposal.[ES] El control predictivo de corrientes de estátor precisa una estimación de las corrientes rotóricas que son, usualmente, no medibles. Estas corrientes afectan a la evolución de la corriente de estátor y por tanto afectan a las predicciones usadas por el controlador. El procedimiento habitual consiste en estimar el efecto que producen dichas corrientes mediante una técnica simple que añade a la predicción el error del periodo de muestreo anterior. En este artí­culo se presenta un nuevo método de estimación basado en el circuito equivalente de la máquina de inducción. Se presentan resultados de simulación y experimentales para el caso particularmente más complejo de una motor polifásico en los que se demuestra la superioridad del método.Este trabajo ha sido financiado por el proyecto RTI2018-101897-B-I00 de FEDER/Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación, y por Junta de Andalucía y Fondos FEDER a través del proyecto P20-00546, España.G. Satué, M.; R. Arahal, M.; R. Ramírez, D. (2022). Estimación de intensidades rotóricas en máquinas polifásicas para control predictivo. Revista Iberoamericana de Automática e Informática industrial. 20(1):25-31. https://doi.org/10.4995/riai.2022.17153253120