During the assembly process of battery packs for electric vehicles, it is crucial to ensure that the cell-to-busbar joints can be produced with high quality, good reliability, and with minimal impact on the individual battery cells. This thesis project investigates the influence of different process parameters on the joint quality for laser welded dissimilar material cell-to-busbar joints. Nickel plated copper and steel plates, joined in an overlap configuration, are used as a simplified geometry, representing a cell-to-busbar joint. By the utilization of artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, the joint quality is predicted and evaluated. The present thesis investigates how a set of optimized process parameters can be identified for the considered laser welded dissimilar material cell-to-busbar joints, in order to simultaneously maximize the interface width for the joints, and minimize the formation of undercuts and resulting in-process temperatures. NSGA-II is used to efficiently search for trade-off solutions, in an meta model-based multi-objective optimization approach, where the meta models are used to approximate the objectives, corresponding to the joint quality obtained from computational fluid dynamics simulations. With this, the time for one objective evaluation is reduced from approximately 9 hours, when the objectives are evaluated directly from computational fluid dynamics simulations, to only tenths of a second. With the proposed optimization approach, the Pareto-optimal front of trade-off solutions is identified, leading to the selection of three optimal solutions for validation. The validity of the proposed optimization approach, and the selected optimal solutions, are confirmed by means of both physical laser welding experiments and computational fluid dynamics simulations. It is shown that the selected optimal solutions, corresponding to three parameter setups, can be used to produce joints with large interface width and low in-process temperatures, without achieving a full penetration in the lower plate of the joint.

In this study, methodologies for modeling grey uncertainty in water resources systems are developed, specifically for the problems in two identified areas in water resources: waste load allocation in streams and floodplain planning. A water resources system is associated with some degree of uncertainty, due to randomness of hydrologic and hydraulic parameters, imprecision and subjectivity in management goals, inappropriateness in model selection, inexactness of different input parameters for inadequacy of data, etc. Uncertainty due to randomness of input parameters could be modeled by the probabilistic models, when probability distributions of the parameters may be estimated. Uncertainties due to imprecision in the management problem may be addressed by the fuzzy decision models. In addition, some parameters in any water resources problems need to be addressed as grey parameters, due to inadequate data for an accurate estimation but with known extreme bounds of the parameter values. Such inexactness or grey uncertainty in the model parameters can be addressed by the inexact or grey optimization models, representing the parameters as interval grey numbers. The research study presented in this thesis deals with the development of grey and fuzzy optimization models, and the combination of the two for water resources systems decision-making. Three grey fuzzy optimization models for waste load allocation, namely (i) Grey Fuzzy Waste Load Allocation Model (GFWLAM), (ii) two-phase GFWLAM and (iii) multiobjective GFWLAM, and a Grey Integer Programming (GIP) model for floodplain planning, are developed in this study. The Grey Fuzzy Waste Load Allocation Model (GFWLAM) for water quality management of river system addresses uncertainty in the membership functions for imprecisely stated management goals of the Pollution Control Agency (PCA) and dischargers. To address the imprecision in fixing the boundaries of membership functions (also known as membership parameters), the membership functions themselves are treated as imprecise in the model and the membership parameters are expressed as interval grey numbers. The conflict between the fuzzy goals of PCA and dischargers is modeled using the concept of fuzzy decision, but because of treating the membership parameters as interval grey numbers, in the present study, the notion of ‘fuzzy decision’ is extended to the notion of ‘grey fuzzy decision’. A terminology ‘grey fuzzy decision’ is used to represent the fuzzy decision resulting from the imprecise membership functions. The model provides flexibility for PCA and dischargers to specify their aspirations independently, as the membership parameters for membership functions are interval grey numbers in place of a deterministic real number. In the solution, optimal fractional removal levels of the pollutants are obtained in the form of interval grey numbers. This enhances the flexibility and applicability in decision-making, as the decision-maker gets a range of optimal solutions for fixing the final decision scheme considering technical and economic feasibility of the pollutant treatment levels. The methodology is demonstrated with the case studies of a hypothetical river system and the Tunga-Bhadra river system in Karnataka, India. Formulation of GFWLAM is based on the approach for solving fuzzy multiple objective optimization problem using max-min as the operator, which usually may not result in a unique solution. The two-phase GFWLAM captures all the alternative optimal solutions of the GFWLAM. The solution technique in the Phase 1 of two-phase GFWLAM is the same as that of GFWLAM. The Phase 2 maximizes upper bounds and minimizes lower bounds of decision variables, keeping the optimal value of goal fulfillment level same as obtained in the Phase 1. The two-phase GFWLAM gives the unique, widest, intervals of the optimal fractional removal levels of pollutant corresponding to the optimal value of goal fulfillment level. The solution increases the widths of interval-valued fractional removal levels of pollutants by capturing all the alternative optimal solutions and thus enhances the flexibility and applicability in decision-making. The model is applied to the case study of Tunga-Bhadra river system, which shows the existence of multiple solutions when the GFWLAM is applied to the same case study. The width of the interval of optimal fractional removal level plays an important role in the GFWLAM, as more width in the fractional removals implies a wider choice to the decision-makers and more applicability in decision-making. The multiobjective GFWLAM maximizes the width of the interval-valued fractional removal levels for providing a latitude in decision-making and minimizes the width of goal fulfillment level for reducing the system uncertainty. The multiobjective GFWLAM gives a new methodology to get a satisfactory deterministic equivalent of a grey fuzzy optimization problem, using the concept of acceptability index for a meaningful ranking between two partially or fully overlapping intervals. The resulting multiobjective optimization model is solved by fuzzy multiobjective optimization technique. The consistency of the solution is verified by solving the problem with fuzzy goal programming technique. The multiobjective GFWLAM avoids intermediate submodels unlike GFWLAM, so that the solution from a single deterministic equivalent of the GFWLAM adequately covers all possible situations. Although the solutions obtained from multiobjective GFWLAM provide more flexibility than those of the GFWLAM, its application is limited to grey fuzzy goals expressed by linear imprecise membership functions only, whereas GFWLAM has the capability to solve the model with any monotonic nonlinear imprecise membership functions also. The methodology is demonstrated with the case studies of a hypothetical river system and the Tunga-Bhadra river system in Karnataka, India. The Grey Integer Programming (GIP) model for floodplain planning is based on the floodplain planning model developed by Lund (2002), to identify an optimal mix of flood damage reduction options with probabilistic flood descriptions. The model demonstrates how the uncertainty of various input parameters in a floodplain planning problem can be modeled using interval grey numbers in the optimization model. The GIP model for floodplain planning does not replace a post-optimality analysis (e.g., sensitivity analysis, dual theory, parametric programming, etc.), but it provides additional information for interpretation of the optimal solutions. The results obtained from GIP model confirm that the GIP is a useful technique for interpretation of the solutions particularly when a number of potential feasible measures are available in a large scale floodplain planning problem. Though the present study does not directly compare the GIP technique with sensitivity analysis, the results indicate that the rigor and extent of post-optimality analyses may be reduced with the use of GIP for a large scale floodplain planning problem. Application of the GIP model is demonstrated with the hypothetical example as presented in Lund (2002).

Made available in DSpace on 2014-12-17T15:47:52Z (GMT). No. of bitstreams: 1 ThatianaCNS_DISSERT.pdf: 1637234 bytes, checksum: 8b38ce4a7a358efe654d9bb1c23c15bc (MD5) Previous issue date: 2010-03-02 / The distribution of petroleum products through pipeline networks is an important problem that arises in production planning of refineries. It consists in determining what will be done in each production stage given a time horizon, concerning the distribution of products from source nodes to demand nodes, passing through intermediate nodes. Constraints concerning storage limits, delivering time, sources availability, limits on sending or receiving, among others, have to be satisfied. This problem can be viewed as a biobjective problem that aims at minimizing the time needed to for transporting the set of packages through the network and the successive transmission of different products in the same pipe is called fragmentation. This work are developed three algorithms that are applied to this problem: the first algorithm is discrete and is based on Particle Swarm Optimization (PSO), with local search procedures and path-relinking proposed as velocity operators, the second and the third algorithms deal of two versions based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed algorithms are compared to other approaches for the same problem, in terms of the solution quality and computational time spent, so that the efficiency of the developed methods can be evaluated / A distribui??o de produtos de petr?leo atrav?s de redes de polidutos ? um importante problema que se coloca no planejamento de produ??o das refinarias. Consiste em determinar o que ser? feito em cada est?gio de produ??o dado um determinado horizonte de tempo, no que respeita ? distribui??o de produtos de n?s fonte ? procura de n?s, passando por n?s intermedi?rios. Restri??es relativas a limites de armazenamento, tempo de entrega, disponibilidade de fontes, limites de envio ou recebimento, entre outros, t?m de ser satisfeitas. Este problema pode ser visto como um problema biobjetivo, que visa minimizar o tempo necess?rio para transportar o conjunto de pacotes atrav?s da rede e o envio sucessivo de produtos diferentes no mesmo duto que ? chamado de fragmenta??o. Neste trabalho, s?o desenvolvidos tr?s algoritmos que s?o aplicados a esse problema: o primeiro algoritmo ? discreto e baseia-se na Otimiza??o por Nuvem de Part?culas (PSO), com procedimentos de busca local e path-relinking propostos como operadores de velocidade, o segundo e o terceiro algoritmos tratam de duas vers?es baseadas no Non-dominated Sorting Genetic Algorithm II (NSGA-II). Os algoritmos propostos s?o comparados a outras abordagens para o mesmo problema, em termos de qualidade de solu??o e tempo computacional despendido, a fim de se avaliar a efici?ncia dos m?todos desenvolvidos

La prise de décision est une partie intégrante de notre vie quotidienne où le décideur est confronté à des problèmes composés de plusieurs objectifs habituellement contradictoires. Dans ce travail, nous traitons des problèmes d'optimisation multiobjectif dans des espaces de recherche continus ou discrets. Nous avons développé plusieurs nouveaux algorithmes basés sur les métaheuristiques hybrides évolutionnaires, en particulier sur l'algorithme MOEA/D. Nous avons proposé l'algorithme HEMH qui utilise l'algorithme DM-GRASP pour construire une population initiale de solutions de bonne qualité dispersées le long de l'ensemble des solutions Pareto optimales. Les résultats expérimentaux montrent la supériorité de toutes les variantes hybrides proposées sur les algorithmes originaux MOEA/D et SPEA2. Malgré ces bons résultats, notre approche possède quelques limitations, levées dans une version améliorée de HEMH : HEMH2 et deux autres variantes HEMHde et HEMHpr. Le Adaptive Binary DE inclus dans les HEMH2 et HEMHde a de meilleures capacités d'exploration qui pallient aux capacités de recherche locale contenues dans la HEMH, HEMH2 et HEMHde. Motivés par ces résultats, nous avons proposé un nouvel algorithme baptisé HESSA pour explorer un espace continu de recherche où le processus de recherche est réalisé par différentes stratégies de recherche. Les résultats expérimentaux montrent la supériorité de HESSA à la fois sur MOEA/D et dMOPSO. Tous les algorithmes proposés ont été vérifiés, testé et comparés à certaines méthodes MOEAs. Les résultats expérimentaux montrent que toutes les propositions sont très compétitives et peuvent être considérés comme une alternative fiable / Many real-world decision making problems consist of several conflicting objectives, the solutions of which is called the Pareto-optimal set. Hybrid metaheuristics proved their efficiency in solving these problems. They tend to enhance search capabilities by incorporating different metaheuristics. Thus, we are concerned with developing new hybrid schemes by incorporating different strategies with exploiting the pros and avoiding the drawback of the original ones. First, HEMH is proposed in which the search process includes two phases DMGRASP obtains an initial set of efficient solutions in the 1st phase. Then, greedy randomized path-relinking with local search or reproduction operators explore the non-visited regions. The efficient solutions explored over the search are collected. Second, a comparative study is developed to study the hybridization of different metaheuristics with MOEA/D. The 1st proposal combines adaptive discrete differential Evolution with MOEA/D. The 2nd combines greedy path-relinking with MOEA/D. The 3rd and the 4th proposals combine both of them in MOEA/D. Third, an improved version of HEMH is presented. HEMH2 uses inverse greedy to build its initial population. Then, differential evolution and path-relink improves these solutions by investigating the non-visited regions in the search space. Also, Pareto adaptive epsilon concept controls the archiving process. Motivated by the obtained results, HESSA is proposed to solve continuous problems. It adopts a pool of search strategies, each of which has a specified success ratio. A new offspring is generated using a randomly selected one. Then, the success ratios are adapted according to the success of the generated offspring. The efficient solutions are collected to act as global guides. The proposed algorithms are verified against the state of the art MOEAs using a set of instances from literature. Results indicate that all proposals are competitive and represent viable alternatives

Methodologies designed to support new product development are receiving increasing interest in recent literature. A significant percentage of new product failure is attributed to a mismatch between designed product features and consumer liking. A variety of methodologies have been proposed and tested for consumer liking or preference prediction, ranging from statistical methodologies e.g. multiple linear regression (MLR) to non-statistical approaches e.g. artificial neural networks (ANN), support vector machines (SVM), and belief rule-based (BRB) systems. BRB has been previously tested for consumer preference prediction and target setting in case studies from the beverages industry. Results have indicated a number of technical and conceptual advantages which BRB holds over the aforementioned alternative approaches. This thesis focuses on presenting further advantages and applications of the BRB methodology for consumer liking prediction. The features and advantages are selected in response to challenges raised by three addressed case studies. The first case study addresses a novel industry for BRB application: the fast moving consumer goods industry, the personal care sector. A series of challenges are tackled. Firstly, stepwise linear regression, principal component analysis and AutoEncoder are tested for predictors’ selection and data reduction. Secondly, an investigation is carried out to analyse the impact of employing complete distributions, instead of averages, for sensory attributes. Moreover, the effect of modelling instrumental measurement error is assessed. The second case study addresses a different product from the personal care sector. A bi-objective prescriptive approach for BRB model structure selection and validation is proposed and tested. Genetic Algorithms and Simulated Annealing are benchmarked against complete enumeration for searching the model structures. A novel criterion based on an adjusted Akaike Information Criterion is designed for identifying the optimal model structure from the Pareto frontier based on two objectives: model complexity and model fit. The third case study introduces yet another novel industry for BRB application: the pastry and confectionary specialties industry. A new prescriptive framework, for rule validation and random training set allocation, is designed and tested. In all case studies, the BRB methodology is compared with the most popular alternative approaches: MLR, ANN, and SVM. The results indicate that BRB outperforms these methodologies both conceptually and in terms of prediction accuracy.

[pt] A programação de produção em refinaria pode ser compreendida como decisões que buscam otimizar alocação de recursos, o sequenciamento de atividades e a sua realização temporal, respeitando restrições e visando ao atendimento de múltiplos objetivos. Apesar da complexidade e natureza combinatória, a atividade carece de sistemas sofisticados que auxiliem o processo decisório, especialmente baseadas em otimização, pois as ferramentas utilizadas são planilhas ou softwares de simulação. A diversidade de objetivos do problema não implica em equivalência de importância. Pode-se considerar que existem grupos, onde os que afetam diretamente a capacidade produtiva da refinaria se sobrepõem aos associados à maior continuidade operacional. Esta tese propõe o desenvolvimento de algoritmos multiobjetivos para programação de petróleo em refinaria. As propostas se baseiam em conceituadas técnicas da literatura multiobjetivo, como dominância de Pareto e decomposição do problema, integradas à programação genética com inspiração quântica. São estudados modelos em um ou dois níveis de decisão. A diferenciação dos grupos de objetivos é avaliada com base em critérios estabelecidos para considerar uma solução proposta como aceitável e também é avaliada a influência de uma população externa no processo evolutivo. Os modelos são testados em cenários de uma refinaria real e os resultados são comparados com um modelo que trata os objetivos de forma hierarquizada. As abordagens baseadas em dominância e em decomposição apresentam vantagem sobre o algoritmo hierarquizado, e a decomposição é superior. Numa comparação com o modelo em dois níveis de decisão, apenas o que utiliza estratégia de decomposição em cada nível apresenta bons resultados. Ao final deste trabalho é obtido mais de um modelo multiobjetivo capaz de oferecer um conjunto de soluções que atendam aos objetivos críticos e deem flexibilidade de análise a posteriori para o programador de produção, o que, por exemplo, permite que ele pondere questões não mapeadas no modelo. / [en] Refinery scheduling can be understood as a set of decisions which aims to optimize resource allocation, task sequencing, and their time-related execution, respecting constraints and targeting multiple objectives. Despite its complexity and combinatorial nature, the refinery scheduling lacks more sophisticated support decision tools. The main systems in the area are worksheets and, sometimes, simulation software. The multiple objectives do not mean they have the same importance. Actually, they can be grouped whereas the objectives related to the refinery production capacity are more important than the ones related to a smooth operation. This thesis proposes the development of multiobjective algorithms applied to crude oil refinery scheduling. The proposals are based on the major technics of multiobjective literature, like Pareto dominance and problem decomposition, integrated with a quantum-inspired genetic programming approach. One and two decision level models are studied. The difference between groups is handled with conditions that define what can be considered a good solution. The effect of using an archive population in the evolutionary process is also evaluated. The results of the proposed models are compared with another model that handles the objectives in a hierarchical logical. Both decomposition and dominance approaches have better results than the hierarchical model. The decomposition model is even better. The bilevel decomposition method is the only one, among two decision levels models, which have shown good performance. In the end, this work achieves more than one multiobjective model able to offer a set of solutions which comprises the critical objectives and can give flexibility to the production scheduler does his analysis. Therefore, he can consider aspects not included in the model, like the forecast of crude oil batches not scheduled yet.

El objetivo de esta Tesis es el diseño de algoritmos robustos y flexibles que permitan automatizar el diseño óptimo de los pórticos de hormigón armado habitualmente empleados en edificación y extraer conclusiones generales sobre las estructuras optimizadas. El trabajo define un esquema general para la optimización monoobjetivo (coste económico) y multiobjetivo de estas estructuras que es aplicado a pórticos planos con un máximo de 153 variables. Entre ellas figuran seis calidades diferentes de hormigón. Para minimizar el coste económico se prueban cinco métodos heurísticos: una Estrategia de Saltos Múltiples Aleatorios (RW), el Gradiente First Best (FB), la Cristalización Simulada (SA), la Aceptación por Umbrales (TA) y los Algoritmos Genéticos (GA). Estas técnicas se utilizan en una primera fase para optimizar un pórtico de dos vanos y cuatro plantas sometido a acciones verticales y horizontables. La versión desarrollada de SA proporciona el diseño de mayor calidad, cuyo coste es de 3473.06 . Los mejores proyectos obtenidos mediante las variantes creadas de TA, FB, GA y RW tienen costes mínimos superiores en un 0.52%, 5.74%, 8.69% y un 124.6% respectivamente. Por estos motivos se elige SA para, en una segunda fase, optimizar económicamente otros pórticos de dos vanos y dos, seis y ocho plantas. Los resultados obtenidos permiten proponer reglas para el predimensionamiento de las estructuras optimizadas y automatizar la elección de los parámetros del algoritmo SA, lo que evita largos procesos de ensayo y error. Se comprueba que los estados límites habitualmente empleados en el diseño de esta tipología estructural son también suficientes para comprobar la seguridad de las estructuras optimizadas. Asimismo se investiga la repercusión económica del empleo de un único tipo de hormigón (un HA-25 con resistencia de proyecto a compresión igual a 25 MPa) y de la utilización de vigas planas en lugar de descolgadas. En el caso del pórtico de ocho plantas, el uso exclusivo / Payá Zaforteza, IJ. (2007). Optimización heuristica de pórticos de edificación de hormigón armado [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/4161

[ES] Actualmente existen numerosas y variadas contribuciones basadas en el ADRC. Por un lado, algunos trabajos abordan la metodología ADRC. Sin embargo, son pocos los que ofrecen una explicación exhaustiva de su diseño y aplicación, dirigida a aquellos investigadores que están empezando a explorar esta estrategia de control. Por otro lado, la sintonización del ADRC y el control compuesto basado en ADRC son áreas de investigación abiertas. Una de las discusiones que se mantiene activa en la literatura está relacionada con la forma de seleccionar los parámetros principales del ADRC de modo que se alcance la estabilidad de lazo cerrado con un rechazo de perturbaciones y robustez apropiadas, especialmente cuando el ADRC se emplea para controlar procesos aproximados mediante representaciones más sencillas como el modelo de primer orden más retardo (FOPDT). Asimismo, la estimación activa de la incertidumbre y las perturbaciones ha hecho atractiva la idea de integrar la topología ADRC con técnicas de control avanzado, por ejemplo, con el control predictivo basado en modelo (MPC). El mayor desafío que surge al realizar esta combinación radica en cómo formular el lazo de control para que el mecanismo de rechazo de perturbaciones del ADRC transforme el comportamiento del sistema controlado en el de una planta deseada simplificada, relajando así el requisito de un modelo detallado y considerando directamente las restricciones en las variables del lazo. Esta tesis presenta tres contribuciones al conocimiento del ADRC para abordar los desafíos expuestos anteriormente. La primera de ellas es una guía para el diseño y aplicación de controladores lineales mediante el control convencional por rechazo activo de perturbaciones. Esta guía ofrece, a modo de tutorial, una revisión de la fundamentación teórica del ADRC y condensa en un algoritmo los pasos para el diseño de estos controladores con el propósito de facilitar su implementación de acuerdo con la formulación del problema en el marco de la estimación y rechazo de perturbaciones y la selección empírica de sus ganancias. La segunda contribución de esta disertación es un conjunto de reglas de sintonía para el cálculo de los tres parámetros distintivos del ADRC con los que se diseñan las ganancias del observador de estados y de la ley de control. Estas reglas permiten sintonizar el ADRC para el control de un proceso aproximado mediante un modelo FOPDT y ofrecen al diseñador diferentes conjuntos de parámetros de acuerdo con un nivel de robustez deseado. Esta contribución se basa en el desarrollo de procedimientos de diseño de optimización multiobjetivo enfocados al control de un grupo de plantas FOPDT nominales. Los resultados de dichos procedimientos se ajustaron a las fórmulas de sintonía proporcionadas. La tercera contribución es una nueva arquitectura de control que combina el mecanismo de rechazo de perturbaciones del ADRC y la estrategia de horizonte deslizante del MPC. En este lazo, una ley de control predictivo gobierna una planta de primer orden más integrador que se induce sobre proceso real sujeto a restricciones. Lo anterior es posible compensando el desajuste entre las plantas real y deseada e incorporando el término de compensación del ADRC en la formulación de las restricciones del controlador predictivo. El bucle pretende proporcionar una solución para controlar sistemas con restricciones para los que no se ha identificado un modelo nominal. Esta disertación está dirigida tanto a los investigadores interesados en explorar el control por rechazo activo de perturbaciones como a aquellos que consideran a esta tecnología como una de sus líneas de investigación principales. Las contribuciones sirven a quienes se inician en el estudio del ADRC, a los diseñadores de controladores que buscan implementar el ADRC lineal considerando el rechazo de perturbaciones de procesos FOPDT y a los investigadores abiertos a la discusión de los beneficios potenciales de de combinar el ADRC con el MPC. / [CAT] Actualment existeixen nombroses i variades contribucions basades en l'ADRC. D'una banda, alguns treballs aborden la metodologia ADRC. No obstant això, són pocs els que ofereixen una explicació exhaustiva del seu disseny i aplicació, dirigida a aquells investigadors que estan començant a explorar aquesta estratègia de control. D'altra banda, la sintonització de l'ADRC i el control compost basat en ADRC són àrees d'investigació obertes. Una de les discussions que es manté activa en la literatura està relacionada amb la manera de seleccionar els paràmetres principals de l'ADRC de manera que s'aconseguisca l'estabilitat de llaç tancat amb un rebuig de pertorbacions i robustesa apropiades, especialment quan l'ADRC s'empra per a controlar processos aproximats mitjançant representacions més senzilles com el model de primer ordre més retard (FOPDT). Així mateix, l'estimació activa de la incertesa i les pertorbacions ha fet atractiva la idea d'integrar la topologia ADRC amb tècniques de control avançat, per exemple, amb el control predictiu basat en model (MPC). El major desafiament que sorgeix en realitzar aquesta combinació radica en com formular el llaç de control perquè el mecanisme de rebuig de pertorbacions de l'ADRC transforme el comportament del sistema controlat en el d'una planta desitjada simplificada, relaxant així el requisit d'un model detallat i considerant directament les restriccions en les variables del llaç. Aquesta tesi presenta tres contribucions al coneixement de l'ADRC per a abordar els desafiaments exposats anteriorment. La primera d'elles és una guia per al disseny i aplicació de controladors lineals mitjançant el control convencional per rebuig actiu de pertorbacions. Aquesta guia ofereix, a manera de tutorial, una revisió de la fonamentació teòrica de l'ADRC i condensa en un algorisme els passos per al disseny d'aquests controladors amb el propòsit de facilitar la seua implementació d'acord amb la formulació del problema en el marc de l'estimació i rebuig de pertorbacions i la selecció empírica dels seus guanys. La segona contribució d'aquesta dissertació és un conjunt de regles de sintonia per al càlcul dels tres paràmetres distintius de l'ADRC amb els quals es dissenyen els guanys de l'observador d'estats i de la llei de control. Aquestes regles permeten sintonitzar l'ADRC per al control d'un procés aproximat mitjançant un model FOPDT i ofereixen al dissenyador diferents conjunts de paràmetres d'acord amb un nivell de robustesa desitjat. Aquesta contribució es basa en el desenvolupament de procediments de disseny d'optimització multiobjectiu enfocats al control d'un grup de plantes FOPDT nominals. Els resultats d'aquests procediments es van ajustar a les fórmules de sintonia proporcionades. La tercera contribució és una nova arquitectura de control que combina el mecanisme de rebuig de pertorbacions de l'ADRC i l'estratègia d'horitzó lliscant del MPC. En aquest llaç, una llei de control predictiu governa una planta de primer ordre més integrador que s'indueix sobre procés real subjecte a restriccions. L'anterior és possible compensant el desajustament entre les plantes real i desitjada i incorporant el terme de compensació de l'ADRC en la formulació de les restriccions del controlador predictiu. El bucle pretén proporcionar una solució per a controlar sistemes amb restriccions per als quals no s'ha identificat un model nominal. Aquesta dissertació està dirigida tant als investigadors interessats a explorar el control per rebuig actiu de pertorbacions com a aquells que consideren a aquesta tecnologia com una de les seues línies d'investigació principals. Les contribucions serveixen als qui s'inicien en l'estudi de l'ADRC, als dissenyadors de controladors que cerquen implementar l'ADRC lineal considerant el rebuig de pertorbacions de processos FOPDT i als investigadors oberts a la discussió dels beneficis potencials de de combinar l'ADRC amb el MPC. / [EN] Numerous and varied contributions based on the ADRC are currently available. On the one hand, some works address the ADRC methodology. Still, only some offer a comprehensive explanation of its design and application aimed at those researchers who are starting to explore this control strategy. On the other hand, the ADRC tuning and the ADRC-based composite control are open research areas. One of the discussions that remain active in the literature is related to how to select the LADRC main parameters so that closed-loop stability is achieved with appropriate disturbance rejection and robustness, mainly when the ADRC is used to control processes approximated by more straightforward representations such as the first-order plus delay (FOPDT) model. Likewise, the active estimation of uncertainty and disturbances has made integrating the ADRC topology with advanced control techniques, like Model-Based Predictive Control (MPC), attractive. The major challenge in realising this combination lies in how to formulate the control loop so that the ADRC disturbance rejection mechanism transforms the behaviour of the controlled system into that of a simplified desired plant, thus relaxing the requirement for a detailed model while directly considering the constraints on the loop variables. This thesis presents three contributions to ADRC knowledge to address the challenges mentioned above. The first is a guide for designing and applying linear controllers using conventional active disturbance rejection control. This guide offers a review of the theoretical foundation of the ADRC. It condenses in an algorithm the steps for designing these control loops to facilitate their implementation according to the problem formulation in the disturbance estimation and rejection framework and the empirical selection of their gains. The second contribution of this dissertation is a set of tuning rules for computing the three distinctive parameters of the ADRC with which the state observer and control law gains are designed. These rules allow tuning the ADRC to control an approximate process using a first-order plus delay model and offer different sets of parameters according to a desired level of robustness. This contribution is based on developing multi-objective optimisation design procedures focused on controlling a group of nominal FOPDT plants. The results of these procedures were fitted to the tuning formulae provided. The third contribution is a new control architecture that combines the disturbance rejection mechanism of the ADRC and the receding horizon strategy of the MPC. In this loop, a predictive control law governs a first-order plus integrator plant enforced on the real process subject to constraints. The above is possible by compensating for the mismatch between the real and desired plants and incorporating the ADRC compensation term in the constraints formulation of the predictive controller. The loop is intended to provide a solution to control constrained systems for which no nominal model has been identified. This dissertation addresses researchers interested in exploring active disturbance rejection control and those considering this technology as one of their main lines of research. The contributions of this dissertation serve those new to the study of ADRC, controller designers seeking to implement linear ADRC by considering the disturbance rejection response of processes approximated using first-order plus delay models, and researchers open to discussing the potential benefits of combining ADRC with advanced techniques such as MPC. / Martínez Carvajal, BV. (2023). Design of controllers based on active disturbance rejection control (ADRC) and its integration with model predictive control (MPC) [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/196581