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[EN] Control education is a mature area in which many professors and researchers have worked hard to face the challenge of providing a versatile education, with a strong scientific base. All this without losing sight of the needs of the industry; adapting the contents, methodologies and tools to the continuous social and technological changes of our time. This article presents a reflection on the role of automation in today s society, a review of the traditional objectives of control education through seminal works in the area and finally a review of the main current trends., [ES] La educación en automática es un área madura en la que multitud de profesores e investigadores han trabajado intensamente para afrontar el reto de proporcionar una educación versátil, con una fuerte base cientí­fica. Todo ello sin perder de vista las necesidades de la industria; adaptando los contenidos, las metodologí­as y las herramientas a los continuos cambios sociales y tecnológicos de nuestro tiempo. Este artí­culo presenta una reflexión sobre el papel de la automática en la sociedad actual, una revisión de los objetivos tradicionales de la educación en automática a través de los trabajos seminales del área y finalmente una revisión de las principales tendencias actuales.

[Resumen] En este trabajo se presenta una nueva herramienta para su uso en docencia en asignaturas del área de Ingeniería de Sistemas y Automática, y por extensión en cualquier materia científico-técnica. Doctus es una versión completamente renovada de Goodle GMS, una aplicación que en los últimos años ha cobrado relevancia en entornos académicos con la que se ha tratado de llevar el concepto de evaluación automática mucho más allá de los paradigmas tradicionales recogidos en Moodle y herramientas afines. La extensa base de datos de ejercicios creados hasta la actualidad en Goodle y compatibles con el nuevo Doctus, permite un acceso rápido a las funcionalidades de autocorrección. Además de presentar una arquitectura software modular orientada a objetos y una interfaz mejorada, Doctus posee una serie de características innovadoras que resultan de interés práctico en el área de Automática. https://doi.org/10.17979/spudc.9788497498081

This paper proposes a robust periodic economic model predictive control (MPC) using probabilistic set invariance for discrete-time descriptor systems. The considered systems are affected by additive disturbances that are bounded and follow a zero-mean Gaussian distribution. To guarantee robustness of the proposed robust economic MPC, a probabilistic invariant set with a given probability is used to contract the state and input constraints. An auxiliary input variable is defined to satisfy algebraic equations from descriptor systems in presence of disturbances. Finally, an application of the proposed MPC strategy to a smart micro-grid is provided to illustrate the performance., This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project DEOCS (ref. DPI2016-76493) and SCAV (ref. DPI2017-88403-R), the FPI grant (ref. BES-2014-068319), and by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (ref. 2017-SGR-482).

Trabajo presentado al 20th IFAC (International Federation of Automatic Control) World Congress, celebrado en Toulouse (Francia) del 9 al 14 de julio de 2017., A periodic nonlinear economic model predictive control (EMPC) with changing prediction horizon is proposed for the optimal management of water distribution networks (WDNs). The control model of the WDN is built by means of nonlinear differential-algebraic equations in which both the hydraulic pressure and flow variables are taken into account. The model allows the controller to consider minimum pressure constraints at the demands. A periodic terminal constraint is employed in order to guarantee closed-loop stability. The prediction horizon is modified on-line in order to guarantee convergence to the optimal periodic trajectory. The proposed control strategy is verified with the case study of the Richmond water network in a realistic hydraulic simulator. Although there are modeling errors between the control model and hydraulic model, the closed-loop system converges to a sub-optimal periodic trajectory satisfying all the constraints., This work has been partially funded by the Spanish Government and FEDER through the projects CICYT ECOCIS (ref. DPI2013-48243), CICYT DEOCS (ref. DPI2016-76493) and CICYT HARCRICS (ref. DPI2014-58104-R).