http://hdl.handle.net/10366/4386 Sun, 26 Apr 2026 09:37:39 GMT 2026-04-26T09:37:39Z http://hdl.handle.net/10366/170417 [ES]El Máster Universitario en Sistemas Inteligentes de la Universidad de Salamanca tiene como misión fundamental introducir a sus estudiantes en el rigor de la investigación científica. En este contexto, el congreso organizado por el Departamento de Informática y Automática se consolida como el escenario idóneo para que los alumnos presenten los resultados de sus Trabajos de Fin de Máster (TFM), sometiéndolos al análisis y debate propios de la comunidad académica. La decimoctava edición del workshop “Avances en Informática y Automática”, celebrada durante el curso 2024 - 2025, ha destacado por su marcado carácter multidisciplinar. En esta ocasión, las investigaciones presentadas abarcan un espectro tecnológico de vanguardia, incluyendo: - Procesamiento de Lenguaje Natural y LLMs: Desde la clasificación de textos en sectores como la hostelería hasta el ajuste fino de mo delos para detectar visualizaciones engañosas. - Machine Learning y Predicción: Aplicaciones prácticas en la previsión de demanda para retail, optimización energética en sistemas intralogísticos y control de alineamiento en sistemas láser de alta potencia. - Visión Artificial y Ciberseguridad: Comparativas de modelos para la detección de violencia y el desarrollo de sistemas IDS/IPS mediante deep learning. - Gestión de Datos y Sociedad: Análisis de espacios de datos en el sector Agrotech, desambiguación de autores en bases bibliográficas y herramientas de IA para facilitar el preprocesamiento de datos a usuarios no expertos. Bajo la supervisión de investigadores de prestigio de la Universidad de Salamanca, este encuentro no solo valida la calidad técnica de los trabajos, sino que sirve de puente hacia la realización de futuras tesis doctorales. Los objetivos principales del evento se mantienen firmes: - Exposición: Brindar a los estudiantes su primera experiencia formal en la difusión de resultados de investigación. - Intercambio: Crear un foro de discusión donde converjan ideas de compañeros, docentes y expertos. - Retroalimentación: Facilitar críticas constructivas que orienten las futuras líneas de investigación de los egresados. - Colaboración: Fortalecer el espíritu de trabajo conjunto y la sinergia entre diferentes áreas de conocimiento. Tue, 10 Mar 2026 00:00:00 GMT http://hdl.handle.net/10366/170417 2026-03-10T00:00:00Z http://hdl.handle.net/10366/170077 [ES] Las agresiones físicas son un problema grave y generalizado, como lo demuestra el hecho de que más de una cuarta parte (27%) de las mujeres de entre 15 y 49 años a nivel global declaran haber sido sometidas a algún tipo de violencia física y/o sexual por parte de su pareja íntima. La Inteligencia Artificial y específicamente las técnicas de Visión Artificial, ofrecen una solución eficaz para detectar la violencia en tiempo real, reduciendo la necesidad de supervisión humana constante. La Inteligencia Artificial, y en particular las técnicas de Visión Artificial, pueden contribuir a identificar episodios de violencia en tiempo real en lugares previamente delimitados, respetando los marcos éticos y legales establecidos. Sin embargo, el aumento del uso de la inteligencia artificial ha generado preocupación sobre la fiabilidad de los algoritmos, lo que ha llevado a la creación de informes destinados a establecer estándares y guías, con organizaciones como la Comisión Europea liderando estos esfuerzos. En este respecto, existen múltiples propuestas de algoritmos para la detección de violencia, donde la combinación de arquitecturas más comúnmente empleada es la de Redes Neuronales Convolucionales (CNN) y Redes de Memoria a Corto y Largo Plazo (LSTM), la cual obtiene excelentes resultados, si bien todavía persisten desafíos; sin embargo, hasta donde se conoce, ningún trabajo en el estado del arte ha abordado la detección de violencia mediante el uso de inteligencia artificial explicable, lo que limita la comprensión y confianza en los resultados obtenidos. Por ello, el objetivo principal de esta Tesis Doctoral es investigar, diseñar, desarrollar y validar algoritmos basados en técnicas de inteligencia artificial fiable orientadas en la detección de violencia en vídeo, con foco en arquitecturas basadas en la combinación de CNN junto con capas LSTM. En base a ello, en este trabajo se ha llevado a cabo un análisis y categorización de todos los procesos que involucran la detección de violencia en vídeo. Posteriormente se han investigado, diseñado, desarrollado y validado tres arquitecturas que utilizan la arquitectura VGG-19 preentrenada, una red neuronal convolucional conocida por su capacidad para extraer características visuales, combinadas con: características manuales, capas LSTM y capas Bi-LSTM. Por último, a partir de estas arquitecturas se han implementado técnicas de inteligencia artificial explicable como GradCAM y se ha creado un algoritmo que cuantifica el nivel de importancia para la detección de violencia por parte de las capas LSTM y Bi-LSTM. Los resultados obtenidos demuestran que el uso de capas Bi-LSTM supera al rendimiento obtenido por capas LSTM, si bien esta mejora no supera el 4% de exactitud. No se han encontrado valores o combinaciones de hiperparámetros para las arquitecturas que utilizan capas LSTM y Bi-LSTM que mejoren de una forma estadísticamente significativa la accuracy obtenida. Las arquitecturas desarrolladas han obtenido buenos reusltados como, por ejemplo, la combinación de VGG-19 preentrenada con capas Bi-LSTM, que obtiene un 97% de exactitud utilizando el dataset Hockey Fights. Por último, se ha conseguido hacer más explicable el proceso de detección con las técnicas implementadas.; [EN] Physical aggressions constitute a serious and widespread issue in society. Studies indicate that in 2015, at least half of the children in Asia, Africa, and North America experienced violence. Although solutions have been explored for medium and long-term interventions, real-time violence detection through artificial intelligence offers a direct and efficient solution that can save lives and reduce the need for constant human supervision. On the other hand, the increasing use of artificial intelligence has raised concerns about the development of reliable algorithms, leading to the creation of reports to define and standardize these terms. Major organizations such as the European Comission are leading this effort. There are multiple algorithm proposals for violence detection, with the most commonly employed combination being Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, which yield excellent results. However, there are still issues to address, such as the actual impact of using LSTM layers instead of just CNN, how much violence detection improves with CNN combined with Bi-LSTM layers instead of LSTM layers, or if certain values and combinations of hyperparameters yield better results. Lastly, the use of reliable artificial intelligence remains very limited. Based on this, this work has developed a systematic literature review with the analysis and categorization of: 21 challenges associated with violence detection, 28 public datasets on violence v´ıdeos, and 13 evaluation metric methods; among others. Three architectures have been developed using pre-trained VGG-19 combined with: manual features, LSTM layers, and Bi-LSTM layers. It is evident that the use of Bi-LSTM layers outperforms the performance obtained by LSTM layers, although this improvement does not exceed 3% accuracy. No values or combinations of hyperparameters that significantly improve the obtained accuracy have been found statistically. The developed architectures have achieved good results, such as the combination of pre-trained VGG-19 with Bi-LSTM layers, which achieves 97% accuracy using the Hockey Fights dataset and 90% using the Violent Flow dataset. Lastly, the use of explainable artificial intelligence techniques on the proposed architectures, where YoloV8 and Frame Difference are used for the extraction of characteristic frames, GradCAM to highlight the areas VGG-19 focuses on for each convolutional layer, and a proprietary algorithm quantifies the level of importance for violence detection by LSTM and Bi-LSTM layers in violence detection. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10366/170077 2025-01-01T00:00:00Z http://hdl.handle.net/10366/169915 [EN]This research aims to conduct a systematic literature review (SLR) on intelligent sensors and the Internet of Things (IoT) in assistive devices for the deaf and hard of hearing. This study analyzes the current state and promise of intelligent sensors in improving the daily lives of those with hearing impairments, addressing the critical need for improved communication and environmental interaction. We investigate the functionality, integration, and use of sensor technologies in assistive devices, assessing their impact on autonomy and quality of life. The key findings show that many sensor-based applications, including vibration detection, ambient sound recognition, and signal processing, lead to more effective and intuitive user experiences. The study emphasizes the importance of energy efficiency, cost-effectiveness, and user-centric design in developing accessible and sustainable assistive solutions. Moreover, it discusses the challenges and future directions in scaling these technologies for widespread adoption, considering the varying needs and preferences of the end-users. Finally, the study advocates for continual innovation and interdisciplinary collaboration in advancing assistive technologies. It highlights the importance of IoT and intelligent sensors in fostering a more inclusive and empowered environment for the deaf and hard-of-hearing people. This review covers studies published between 2011 and 2024, highlighting advances in sensor technologies for assistive systems in this timeframe. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/10366/169915 2024-01-01T00:00:00Z http://hdl.handle.net/10366/169914 [EN]This research presents software for empirically analyzing Java Virtual Machine (JVM) parameter configurations to enhance web application performance. Using tools like JMeter and cAdvisor in a controlled hardware environment, it collects and analyzes performance metrics. Tailored JVM settings for high request loads improved CPU efficiency by 20% and reduced memory usage by 15% compared to standard configurations. For I/O intensive operations with large files, optimized JVM configurations decreased response times by 30% and CPU usage by 25%. These findings highlight the impact of tailored JVM settings on application responsiveness and resource management, providing valuable guidance for developers and engineers. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/10366/169914 2024-01-01T00:00:00Z http://hdl.handle.net/10366/169913 [EN]In recent years, the utilization of augmented reality (AR) and virtual reality (VR) has emerged as a transformative approach in education, revolutionizing traditional teaching methods. This study seeks to explore the efficacy of AR and VR as pedagogical resources for enhancing the teaching of the solar system. The research process involved the development of an application comprising two modules, AR and VR, which were evaluated to assess their impact on the teaching process. Furthermore, a comparative study was conducted to evaluate the immersiveness, interactivity, and ease of use offered by these technologies. The findings demonstrate that both AR and VR demonstrate promise in supporting the teaching process, with the VR module garnering particularly positive evaluations. However, it is crucial to acknowledge existing barriers in underprivileged communities, where public schools face limited investments in technology infrastructure. These limitations hinder the widespread implementation of such immersive experiences and their potential to foster new knowledge acquisition. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/10366/169913 2023-01-01T00:00:00Z http://hdl.handle.net/10366/169912 [EN]There is growing concern among users of computer systems about how their data is handled. In this sense, IT (Information Technology) professionals are not unaware of this problem and are looking for solutions to meet the requirements and concerns of their users. During the last few years, various techniques and technologies have emerged that allow us to answer to the problem posed by users. Technologies such as edge computing and techniques such as one-shot learning and data augmentation enable progress in this regard. Thus, in this article, we propose the creation of a system that makes use of these techniques and technologies to solve the problem of face recognition and form a low-cost security system. The results obtained show that the combination of these techniques is effective in most of the face detection algorithms and allows an effective solution to the problem raised. Sat, 01 Jan 2022 00:00:00 GMT http://hdl.handle.net/10366/169912 2022-01-01T00:00:00Z http://hdl.handle.net/10366/169911 [EN]Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for mapping zones in the city of Itajaí (SC), Brazil. The solution proposes to aid object recognition employing object-based classifiers OneR, NaiveBayes, J48, IBk, and Hoeffding Tree algorithms used together with GeoDMA, and a first approach in the use of Region-based Convolutional Neural Network (R-CNN) and the YOLO algorithm. All this is to characterize vegetation zones, exposed soil zones, asphalt, and buildings within an urban and rural area. Through the implemented model for active identification of geospatial objects with similarity levels, it was possible to apply the data crossover after detecting the best classifier with accuracy (85%) and the kappa agreement coefficient (76%). The case study presents the dynamics of urban and rural expansion, where expressive volumes of data are obtained and submitted to different methods of cataloging and preparation to subsidize rapid control actions. Finally, the research describes a practical and systematic approach, evaluating the extraction of information to the recommendation of knowledge with greater scientific relevance. Allowing the methods presented to apply the calibration of values for each object, to achieve results with greater accuracy, which is proposed to help improve conservation and management decisions related to the zones within the city, leaving as a legacy the construction of a minimum technological infrastructure to support the decision. Fri, 23 Dec 2022 00:00:00 GMT http://hdl.handle.net/10366/169911 2022-12-23T00:00:00Z http://hdl.handle.net/10366/169769 [EN] With the introduction of the Internet of Things, maintenance practices have been moving from reactive to proactive and predictive approaches. The identification of faults often relies on the analysis of real-time data provided by streams and unstructured sources. Ontologies have been applied to the maintenance field, adding a semantic layer to the data that facilitates interoperability and semantic data mining processes. In such a time-sensitive domain, it is important that ontologies go beyond static representations of the domain and allow not only for the incorporation of time related knowledge, but must also be able to adapt to new knowledge and evolve. Evolving an ontology involves re-learning, re-enriching and re-validating knowledge in the face of changes to the domain, and techniques applied for them can be adapted to ontology evolution. This thesis aims to contribute to these fields by using streams of ontology individuals as the trigger for ontology evolution processes – facing challenges tied to the incomplete and transient nature of these data. As such, this thesis introduces an architecture for time-constrained ontology evolution called TICO, or Time Constrained instance-guided Ontology evolution. New versions of ontology classes and properties are reified through a 4D-Fluents approach, thus allowing reasoning over old data and accessing older conceptualizations of the domain. For the identification of property axioms, the possibilistic approach to axiom scoring was adapted to a scenario in which it is not always possible to query all individuals at once. Results show the effectiveness of the approach in accepting/rejecting axioms for the ontology’s properties. To identify patterns in data that could trigger the creation of new classes and enrich existing ones, a Formal Concept Analysis-based approach is employed. Using two different concept lattices that are updated with each individual, it is possible to identify a set of axioms to add to the ontology and uncover implicit relationships between old and new classes.; [ES] Con la introducción del IoT, las prácticas de mantenimiento han ido pasando de orientaciones reactivas a proactivas y predictivas. La identificación de fallas a menudo se basa en el análisis de datos en tiempo real proporcionados por flujos y fuentes no estructuradas. Las ontologías se han aplicado al campo del mantenimiento, añadiendo una capa semántica a los datos que facilita la interoperabilidad y los procesos de minería semántica de datos. En un ámbito tan sensible al tiempo, es importante que las ontologías ultrapasen las representaciones estáticas del dominio y permitan no sólo incorporar conocimientos relacionados con el tiempo, sino que también deben ser capaces de adaptarse y evolucionar. Evolucionar una ontología implica reaprender, re-enriquecer y re-validar el conocimiento y las técnicas aplicadas para ellas pueden adaptarse a la evolución de ontologías. Esta tesis pretende contribuir a estos campos utilizando flujos de individuos RDF como desencadenante de procesos de evolución de ontologías, enfrentándose a retos ligados a la naturaleza incompleta y transitoria de estos datos. Como tal, esta tesis introduce una arquitectura para la evolución de ontologías limitada en el tiempo llamada TICO (Time Constrained instance-guided Ontology evolution). Las nuevas versiones de las clases y propiedades de la ontología se reifican mediante 4D-Fluents, lo que permite razonar sobre datos antiguos y acceder a conceptualizaciones anteriores del dominio. Para la identificación de axiomas de propiedades, se adaptó el enfoque posibilista de cualificación de axiomas a un escenario en el que no siempre es posible obtener la descripción completa del conjunto de datos. Los resultados muestran la eficacia de la solución en aceptar/rechazar axiomas para las propiedades de la ontología. Para identificar patrones en los datos, crear nuevas clases y enriquecer las existentes, se emplea un enfoque basado en el Análisis Conceptual Formal. Utilizando dos redes de conceptos diferentes, es posible identificar un conjunto de axiomas para añadir a la ontología y descubrir relaciones implícitas entre clases antiguas y nuevas. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10366/169769 2025-01-01T00:00:00Z http://hdl.handle.net/10366/169381 [EN] This paper presents some efficient suboptimal detectors, based on statistical descriptors, which take advantage of the high-resolution characteristics of the high-resolution radars (HRR). Which are one of the first stages of the sensor-based localization and tracking technologies. The detection performance has been studied under noise and sea clutter conditions, with non-coherent data from both real and synthetic extended targets. We have also made an adaptation of the classical moving window detection technique for the high-resolution radars, making use of it as a reference technique to evaluate the results obtained with the detection techniques that we present. The experimental results were obtained with the ARIES radar, a maritime surface surveillance LFM-CW HRR operating in X-band. Fri, 24 Aug 2012 00:00:00 GMT http://hdl.handle.net/10366/169381 2012-08-24T00:00:00Z http://hdl.handle.net/10366/169379 [EN] With oil reserves running out, silicon solar cells offer an alternative source of energy. How do they work and how can we exploit their full potential? Indirectly, the Sun is the source of most of the energy we use on Earth: not only of fossil fuels and biomass, but also wind and tidal energy, to mention just a few. Increasingly, there is interest in capturing the energy from the Sun more directly, using photovoltaic cells. A relatively old, medium-sized star made of hot plasma, the Sun radiates energy as electromagnetic radiation over a wide spectrum. At a distance of 150 million kilometres, our planet receives an irradiance of around 1366 W/m2 (1 W= 1 J·s) from the Sun, but not all of this actually reaches us because Earth’s atmosphere reflects and absorbs about 30 % of this energy. Nonetheless, every square metre of Earth’s surface receives an average of nearly 1000 Joules per second from the Sun. To put this into perspective, the total energy consumed globally in 2010 was around 5 x 1020 J. If we assume that our planet is a perfect sphere with a radius of 6370 km, Earth receives 1.8 x 1017 J/s, of which about 1.3 x 1017 J/s reaches Earth’s surface. Thus in one hour, the Sun provides Earth with all the energy we need for a whole year. It isn’t quite that simple, however. Due to meteorological factors, the Sun’s declination and Earth’s rotation, the irradiance is actually closer to 230 W/m2. If we repeat the last calculation using that figure, the time needed to power Earth with energy from the Sun for a year is about five and a half hours – still an impressively short time. Solar radiation is therefore a promising energy reservoir, but how can we collect it and use it? Tue, 22 May 2012 00:00:00 GMT http://hdl.handle.net/10366/169379 2012-05-22T00:00:00Z http://hdl.handle.net/10366/169377 In this paper we report on an experimental study of the spectral response of different compounds in the THz range. We set up a THz Time Domain Spectroscopy System (TDS) that uses a Ti:Sapphire femtosecond laser and a pair of LTD GaAs photoconductive antennas for THz generation and detection. The THz TDS set up was validated through the matching of the main lines of the water vapor spectra in the 0.1-3THz range against the HITRAN database. Finally, the THz spectra of two commercial chemical compounds (paracetamol and ibuprofen) were obtained; the large difference found between the two spectra will easily allow to distinguish between both substances. Further work will be necessary to understand the fingerprints of those substances. Fri, 14 Sep 2012 00:00:00 GMT http://hdl.handle.net/10366/169377 2012-09-14T00:00:00Z http://hdl.handle.net/10366/169332 [EN]This paper treats a comparative study of neural networks and fuzzy neural networks used to model a complex biotechnological process: an activated sludge process taken from a real wastewater treatment plant. The neural networks used in this work are a multiplayer perceptron network and two recurrent neural networks: the Elman one and a neural network that represents the state space model. And other two fuzzy neural networks: the ANFIS network that calculates a Takagy-Sugeno type fuzzy logic system and a neurofuzzy system called FasArt, which is based on the Adaptive Resonance Theory (ART) but it also introduces fonnalisms from the fuzzy set theory. A comparative study of the five networks is carried out using real data collected fonn the plant in order to identify the dynamic behaviour ofthe sludge process in the wastewater plant. Wed, 01 Jan 2003 00:00:00 GMT http://hdl.handle.net/10366/169332 2003-01-01T00:00:00Z http://hdl.handle.net/10366/169331 [EN]A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. The proposed control law allows us to evaluate the control signal in an analytical way, each sampling time being a nonlinear and fuzzy alternative to other classic predictive controllers. The control law is based on the formalization of the internal fuzzy predictive model of the process as linear time-varying state space equations that are updated every discrete time instant to take into account the nonlinearity effects due to disturbance action and changes in the operating point with time. The model is then used to evaluate the predictions, and, taking them as a starting point and considering them as a paradigm of the predictive functional control strategy, a control law, it is derived in an analytical and explicit way by imposing on the outputs of the follow-up of certain reference trajectories previously established. The work presented here addresses the application of this particular strategy of intelligent predictive control to the case of an activated sludge wastewater treatment process successfully in a simulation environment of a real plant taking into account real data for the disturbance records. Such a process is multivariable, nonlinear, time varying, and difficult to control due to its biological nature. The proposed control law can be straightforwardly used within a dual-mode MPC scheme to handle constraints, as a nonlinear and fuzzy alternative to the classic state feedback control law. Tue, 01 Jan 2019 00:00:00 GMT http://hdl.handle.net/10366/169331 2019-01-01T00:00:00Z http://hdl.handle.net/10366/169330 [EN]This paper presents a procedure for the closed-loop stability analysis of a certain variant of the strategy called Fuzzy Model-Based Predictive Control (FMBPC), with a model of the Takagi-Sugeno type, applied to the wastewater treatment process known as the Activated Sludge Process (ASP), with the aim of simultaneously controlling the substrate concentration in the effluent (one of the main variables that should be limited according to environmental legislations) and the biomass concentration in the reactor. This case study was chosen both for its environmental relevance and for special process characteristics that are of great interest in the field of nonlinear control, such as strong nonlinearity, multivariable nature, and its complex dynamics, a consequence of its biological nature. The stability analysis, both of fuzzy systems (FS) and the very diverse existing strategies of nonlinear predictive control (NLMPC), is in general a mathematically laborious task and difficult to generalize, especially for processes with complex dynamics. To try to minimize these difficulties, in this article, the focus was placed on the mathematical simplification of the problem, both with regard to the mathematical model of the process and the stability analysis procedures. Regarding the mathematical model, a state-space model of discrete linear time-varying (DLTV), equivalent to the starting fuzzy model (previously identified), was chosen as the base model. Furthermore, in a later step, the DLTV model was approximated to a local model of type discrete linear time-invariant (DLTI). As regards the stability analysis itself, a computational method was developed that greatly simplified this difficult task (in a local environment of an operating point), compared to other existing methods in the literature. The use of the proposed method provides useful conclusions for the closed-loop stability analysis of the considered FMBPC strategy, applied to an ASP process; at the same time, the possibility that the method may be useful in a more general way, for similar fuzzy and predictive strategies, and for other complex processes, was observed. Fri, 01 Jan 2021 00:00:00 GMT http://hdl.handle.net/10366/169330 2021-01-01T00:00:00Z http://hdl.handle.net/10366/169329 [ES]En este trabajo se aborda la integración de dos métodos o estrategias de Control Predictivo basado en Modelos, a saber: Control Predictivo basado en Modelos Borrosos (FMBPC) y Control Predictivo en Lazo Cerrado (CLP MPC). La primera de estas estrategias utiliza principios de Control Predictivo Funcional (PFC) y está enmarcada, al mismo tiempo, en el ámbito del Control Inteligente (IC). La integración tiene como principal objetivo proporcionar a la estrategia de control no lineal FMBPC un procedimiento de optimización que permita el manejo automático de restricciones en la variable de control. La solución propuesta consiste en hacer uso de una estructura complementaria de tipo CLP MPC para determinar mediante optimización, en cada instante de muestreo, los valores óptimos de un cierto término aditivo, a sumar a la ley de control FMBPC, de tal modo que se satisfagan las restricciones. El modelo de predicciones y la ley de control base necesarios para realizar los cálculos en la estructura CLP MPC son proporcionados por la estrategia FMBPC. La estrategia mixta FMBPC/CLP propuesta ha sido validada, en simulación, aplicándola al control de fangos activados en plantas de tratamiento de aguas residuales (EDAR), poniendo el foco en la imposición de restricciones a la acción de control. Los resultados obtenidos son satisfactorios, observando un buen rendimiento del algoritmo de control diseñado, al tiempo que se garantiza tanto la satisfacción de las restricciones, que era el principal objetivo, como la estabilidad del sistema en lazo cerrado. Fri, 01 Jan 2021 00:00:00 GMT http://hdl.handle.net/10366/169329 2021-01-01T00:00:00Z http://hdl.handle.net/10366/169328 [EN]The control of wastewater treatment plants (WWTPs) is an ecologically, economically, and socially important objective. In the case of plants using activated sludge (ASP) processes, their control presents a significant challenge due to the complexity of the dynamics of these processes (a consequence of their biological nature). To objectively evaluate control strategies, standardized benchmark simulation models (BSMs) are used. This article tests the feasibility and evaluates the performance, in a simulation environment, of a specific fuzzy model-based predictive control strategy, called FMBPC/CLP, applied to the BSM1 reference model. In each iteration, this strategy first uses an FMBPC-type algorithm, which determines the basic control action (based on a fuzzy model and applying functional predictive control) that guarantees the local stability of the closed-loop system. Then, a second predictive control algorithm, called closed-loop predictive control (CLP-MPC), calculates a compensating term that is added to the basic control law and ensures compliance with constraints in the control action. In the simulation experiments carried out, the plant structure described in the BSM1 benchmark (reactor divided into five tanks, followed by a settling tank) was maintained, but the default control configuration was modified. The alternative control configuration designed for the BSM1 test bench includes two control loops: one to regulate the oxygen concentration in compartment 5 of the reactor (maintaining the PI algorithm of the default control configuration) and another loop to regulate the nitrate concentration (nitrate and nitrite) in tank 2 and, simultaneously, the ammonia concentration in tank 5, using the alternative FMBPC/CLP strategy. This control hybrid configuration was tested and evaluated considering values of the influent (dry, rainy, and stormy weather), and performance measurement criteria, both standardized in the BSM1 platform. The base model of the plant to be controlled, necessary for the FMBPC strategy, is obtained by prior fuzzy identification, from open-loop input and output data. The identification is achieved with the help of a software tool that uses mathematical clustering methods (based on the Gustafson–Kessel algorithm) that allow for the extraction of fuzzy models of the Takagi–Sugeno type from the numerical input–output data of a given plant. The FMBPC strategy is potentially appropriate for the control of complex, changing or unknown systems and this article demonstrates that this strategy is viable, with satisfactory performance, and that it can even be competitive when compared with more traditional control strategies. Fri, 26 Dec 2025 00:00:00 GMT http://hdl.handle.net/10366/169328 2025-12-26T00:00:00Z