The maneuverability and freedom provided by unmanned aerial vehicles (UAVs) make these an interesting choice for transporting objects in settings such as search and rescue operations, construction, and smart factories. A commonly proposed method of transport is by using cables attached between each UAV and the payload. However, the geometrical constraints posed by these attachments typically result in a system with highly complex dynamics. Although not an issue for conventional PID control schemes, these complex dynamics make the direct application of model predictive controllers (MPCs) infeasible for real-time usage. For this reason, much of the previous work has focused on treating the payload as a disturbance, thereby losing the ability to predict its effect on the UAVs. Contrary to this, this thesis presents an MPC that both captures the dynamics of the payload, and is capable of real-time usage. This is made possible by a parametrized linearization of the original system, and results in greatly improved performance compared to the disturbance model approach. The controller is derived for a system with two UAVs that transport a bar-like payload and verified both in simulations and physical experiments. The resulting control system is able track a multitude of setpoints, including rotations of both payload and UAVs, as well as lateral translations. Furthermore, it is able to attenuate external disturbances well, and dampens and prevents oscillations more efficiently when compared to the disturbance based approach. The resulting MPC solving time is on the order of milliseconds. Additionally, an initial attempt to decentralize the system is made, and the resulting controller experimentally tested on the UAV–bar system, resulting in a lower MPC solving time (2:5 times faster on average), but worsened performance in terms of position tracking of the bar. / Den manövrerbarhet och frihet som möjliggörs av användandet utav obemannade luftfarkoster (drönare) gör dessa till tämligen intressanta kandidater för lasttransport inom områden såsom sök- och räddningsuppdrag, byggnadskonstruktion och s.k. smarta fabriker. En vanligen förespråkad transportmetod består utav att förse systemet med kablar som fästs mellan last och drönare. De geometriska restriktioner som denna lastkoppling innebär resulterar emellertid ofta i system med väldigt komplicerad dynamik och interaktionskrafter. Även om detta inte innebär något problem för konventionella PID reglersystem så omöjliggör detta det direkta applicerandet utav modellprediktiv reglering (MPC) för realtidsbruk. Av denna anledning har tidigare verk fokuserat på att behandla lasten och dess inverkan på drönarna som en störning, men med detta därmed förlorat möjligheten att förutspå dess effekt på drönarna. I kontrast till detta, kommer det i detta verk att presenteras en MPC som både fångar lastens dynamik och är snabb nog för realtidsanvändning. Detta görs möjligt utav en parametriserad linjärisering utav originalsystemet och ger märkbart bättre resultat än den störningbaserade modellen. Reglersystemet appliceras på ett system bestående utav två drönare och en stång-liknande last och resultatet verifieras både i form av numeriska simuleringar och fysiska experiment. Det resulterande systemet klarar av både rotationer utav last och drönare samt translationer i alla riktningar. Dessutom är systemet kapabelt att hantera externa störningar och både dämpar och förhindrar oscillationer bättre i jämförelse med reglersystem baserat på störningsmodeller. Lösningstiden för MPC-regulatorn är i storleksordningen millisekunder. Utöver detta görs ett initialt försök i att decentralisera tidigare nämnda MPC och det resulterande reglersystemet utvärderas experimentellt på samma drönarsystem som tidigare. Detta resulterar i en lägre lösningstid (2.5 ggr snabbare i genomsnitt), men även i försämrad prestanda med avseende på reglering av stångens position.

<p>This thesis considers the problems of modelling and control of batch processes, a class of finite duration chemical processes characterized by their absence of equilibrium conditions and nonlinear, time-varying dynamics over a wide range of operating conditions. In contrast to continuous processes, the control objective in batch processes is to achieve a non-equilibrium desired end-point or product quality by the batch termination time. However, the distinguishing features of batch processes complicate their control problem and call for dedicated modelling and control tools. In the initial phase of this research, a predictive controller based on the novel concept of reverse-time reachability regions (RTRRs) is developed. Defined as the set of states from where the process can be steered inside a desired end-point neighbourhood by batch termination subject to input constraints and model uncertainties, an algorithm is developed to characterize these sets at each sampling instance offline; these characterizations subsequently play an integral role in the control design. A key feature of the resultant controller is that it requires the online computation of only the immediate control action while guaranteeing reachability to the desired end-point neighbourhood, rendering the control problem efficiently solvable even when using the nonlinear process model. Moreover, the use of RTRRs and one-step ahead type control policy embeds important fault-tolerant characteristics into the controller. Next, we address the problem of the unavailability of reliable and computationally manageable first-principles-based process models by developing a new data-based modelling approach. In this approach, local linear models (identified via latent variable regression techniques) are combined with weights (arising from fuzzy c-means clustering) to describe global nonlinear process dynamics. Nonlinearities are captured through the appropriate combination of the different models while the linearity of the individual models prevents against a computationally expensive predictive controller. This modelling approach is also generalized to account for time-varying dynamics by incorporating online learning ability into the model, making it adaptive. This is accomplished by developing a probabilistic recursive least squares (PRLS) algorithm for updating a subset of the model parameters. The data-based modelling approach is first used to generate data-based reverse-time reachability regions (RTRRs), which are subsequently incorporated in a new predictive controller. Next, the modelling approach is applied on a complex nylon-6,6 batch polymerization process in order to design a trajectory tracking predictive controller for the key process outputs. Through simulations, the modelling approach is shown to capture the major process nonlinearities and closed-loop results demonstrate the advantages of the proposed controller over existing options. Through further simulation studies, model adaptation (via the PRLS algorithm) is shown to be crucial for achieving acceptable control performance when encountering large disturbances in the initial conditions. Finally, we consider the problem of direct quality control even when there are limited quality-related measurements available from the process; this situation typically calls for indirectly pursuing the control objective through trajectory tracking control. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behaviour until batch termination. This "missing data" problem is handled by integrating the previously developed data-based modelling approach with the inferential model in a predictive control framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via simulations of the nylon-6,6 batch polymerization process with a different control objective than considered previously.</p> / Doctor of Philosophy (PhD)

<p>Process operability represents the ability of a process plant to operate satisfactorily away from the nominal operating or design condition, where flexibility and dynamic operability are two important attributes of operability considered in this thesis. Today's companies are facing numerous challenges, many as a result of volatile market conditions. Key to sustainable profitable operation is a robust process supply chain. Within a wider business context, flexibility and responsiveness, i.e. dynamic operability, are regarded as key qualifications of a robust process supply chain.</p> <p>The first part of this thesis develops methodologies to rigorously evaluate the dynamic operability and flexibility of a process supply chain. A model is developed which describes the response dynamics of a multi-product, multi-echelon supply chain system. Its incorporation within a dynamic operability analysis framework is shown, where a bi-criterion, two-stage stochastic programming approach is applied for the treatment of demand uncertainty, and for estimating the Pareto frontier between an economic and responsiveness criterion. Two case studies are presented to demonstrate the effect of supply chain design features on responsiveness. This thesis has also extended current paradigms for process flexibility analysis to supply chains. The flexibility analysis framework, where a steady-state supply chain model is considered, evaluates the ability to sustain feasible steady-state operation for a range of demand uncertainty.</p> <p>The second part of this thesis develops a decision-support tool for supply chain management (SCM), by means of a robust model predictive control (MPC) strategy. An effective decision-support tool can fully leverage the qualifications from the operability analysis. The MPC formulation proposed in this thesis: (i) captures uncertainty in model parameters and demand by stochastic programming, (ii) accommodates hybrid process systems with decisions governed by logical conditions/rulesets, (iii) addresses multiple supply chain performance metrics including customer service and economics, and (iv) considers both open-loop and closed-loop prediction of uncertainty propagation. The developed robust framework is applied for the control of a multi-echelon, multi-product supply chain, and provides a substantial reduction in the occurrence of back orders when compared with a nominal MPC framework.</p> / Master of Applied Science (MASc)

The freight transport plays an important role in the development of the economy. However, this comes with an important contribution to greenhouse gas emission. Recently a shift toward heavy-duty electric vehicles has been made, but some issues still need to be tackled. One of them is to develop ways to quickly recharge the vehicle’s batteries without damaging them. In this thesis, we highlight that not only the current but also the battery temperature need to be carefully managed in order to prevent damages during a charging session. To show that, an electrical, thermal and aging model of Li-ion battery is developed. A charging strategy based on a Model Predictive Control algorithm is proposed. The algorithm controls both the battery current and cooling system in order to achieve the optimal balance between the charging speed and the preservation of the battery. The resulting algorithm is tested, in simulation, against a conventional constant current charging in different charging scenarios. The results show an important increase in performance and highlight the role of the battery cooling system in the preservation of the battery. / Godstransporterna spelar en viktig roll för ekonomins utveckling. Detta innebär dock ett betydande bidrag till utsläppen av växthusgaser. På senare tid har en övergång till tunga elfordon skett, men vissa frågor måste fortfarande lösas. En av dem är att utveckla metoder för att snabbt ladda fordonsbatterierna utan att skada dem. I den här avhandlingen lyfter vi fram att inte bara strömmen utan även batteritemperaturen måste hanteras noggrant för att förhindra skador under en laddning. För att visa detta utvecklas en elektrisk, termisk och åldrande modell för Li-ion-batterier. En laddningsstrategi baserad på en algoritm för modellförutsägbar styrning föreslås. Algoritmen styr både batteriströmmen och kylsystemet för att uppnå en optimal balans mellan laddningshastighet och bevarande av batteriet. Den resulterande algoritmen testas i simulering mot en konventionell konstantström laddning i olika laddningsscenarier. Resultaten visar en betydande ökning av prestanda och belyser batterikylsystemets betydelse för bevarandet av batteriet.

<p dir="ltr">Supervisory predictive control of residential building heating, ventilation, and air conditioning (HVAC) systems could protect electrical infrastructure, enhance occupants’ thermal comfort, reduce energy costs, and minimize emissions. However, there are few experimental demonstrations, with most of the work focusing on simulation studies. To convince stakeholders of the benefits of supervisory predictive controls for residential HVAC systems, it is important to demonstrate practical systems in real buildings. Practical demonstrations also further our understanding of the field performance of these systems. This thesis presents the first comprehensive review of supervisory predictive control experiments in residential buildings, drawing critical insights on the estimated energy savings, the types of equipment controlled, the objectives and problem formulations considered, and other practical considerations. To address limitations in the existing body of experimental work, a series of field demonstrations were performed in a real house with student occupants near the Purdue campus in West Lafayette, Indiana, U.S.A.</p><p dir="ltr">The first field demonstration involved supervisory predictive control of an air-to-air heat pump with backup electric resistance heat. This was the first experiment to consider this equipment configuration, which is common in North America. A simple data-driven method is presented for learning a model of the temperature dynamics of a detached residential building. Using this model, the control system adjusts indoor temperature set points based on weather forecasts, occupancy conditions, and data-driven models of the heating equipment. Field tests from January to March of 2023 included outdoor temperatures as low as −15 ℃. During these tests, the control system reduced total heating energy costs by 19% on average (95% confidence interval: 13–24%) and energy used for backup heat by 38%. The control system also reduced the frequency of using high-stage (19 kW) backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. These real-world results could strengthen the case for deploying predictive home heating control, bringing the technology one step closer to reducing emissions, utility bills, and power grid impacts at scale.</p><p dir="ltr">The second field demonstration advanced the state of the art of predictive residential cooling control, wherein past experimental demonstrations relied on “sensible” models of building thermal dynamics and neglected humidity effects. In this thesis, a model-free machine learning method is introduced to predict the indoor wet-bulb temperature and the sensible heat ratio in a “latent” model formulation, with the aim to increase the accuracy of the real electrical power prediction. The latent and sensible formulations are tested in two separate model predictive controller (MPC) schemes in an on-off fashion. One MPCscheme aims to reduce energy costs while enhancing comfort. The other is a power-limiting controller that aims to keep the power of the HVAC equipment below 2.5 kW between 4 PM and 8 PM. The two MPC schemes and the two load models are assessed through 38 days of testing. It is found that across both economic MPC and power-limiting MPC, the energy savings across the latent and sensible formulations are similar. Through a normalized Cooling Degrees Days analysis, the energy savings to the baseline controller in the house are found to be 16 to 32% for economic MPC (95% confidence interval) and -5 to 10% for power-limiting MPC, with 7 to 21% savings across both controllers (14% mean). For power limiting, the latent formulation reduced the total duration of constraint violation by 88% and the sensible formulation by 40%, with respect to the non-MPC baseline. Additionally, the latent formulation reduced the peak power demand by 13% relative to the baseline, a behavior not observed in the sensible formulation.</p><p dir="ltr">The third field experiment investigated the problem of protecting home electrical infrastructure in the context of electrification retrofits. Installing electric appliances or vehicle charging in a residential building can sharply increase the electric current draws. In older housing, high current draws can jeopardize circuit breaker panels or electrical service (the wires that connect a building to the distribution grid). Upgrading electrical panels or service often entails long delays and high costs, and thus it poses a significant barrier to electrification. This thesis develops and field tests a novel control system that avoids the need for electrical upgrades by maintaining an electrified home’s total current draw within the safe limits of its existing panel and service. In the proposed control architecture, a high-level controller plans device set-points over a rolling prediction horizon, while a low-level controller monitors real-time conditions and ramps down devices if necessary. The control system was tested for 31 consecutive winter days with outdoor temperatures as low as -20 ℃. The control system maintained the whole-home current within the safe limits of electrical panels and service rated at 100 A, a common rating for older houses in North America, by adjusting only the temperature set-points of the heat pump and water heater. Simulations suggest that the same 100 A limit could accommodate a second electric vehicle (EV) with Level II (11.5 kW) charging. The proposed control system could allow older homes to safely electrify without upgrading electrical panels or service, saving a typical household on the order of $2,000 to $10,000. </p><p dir="ltr">These three field experiments demonstrate that low-cost predictive control systems can serve multiple objectives, improving the efficiency of heat pumps and water heaters while maintaining comfort and protecting electrical infrastructure. Future work will be directed toward improving the scalability of these proposed controllers through the incorporation of data-driven methodologies such as data-enabled predictive control, as well as understanding the application of these algorithms with different systems, including batteries, on-site solar photovoltaics, and electrical vehicle charging.</p>

The aim of this project was to compare two control algorithms designed to steer an autonomous vehicle. The comparison was made using a simulated environment to evaluate the performance of both controllers. The simulation used in this project was designed in Python and used an algorithm which randomly constructed roads from predefined road segments to create paths for the vehicle to follow. In this environment the Linear Time Varying (LTV)-Model Predictive Controller (MPC) and Pure Pursuit Controller (PPC) algorithms were evaluated. The thesis compared how well they follow paths, the average control cost of completing tasks, how well they handle input constraints, and the computational time for each algorithm. The data was collected by driving along three sets of randomly generated roads with both control algorithms. One set mostly straight, one with some turns, and one with mostly turns. An Analysis of Variance (ANOVA) test was used to make the comparison between the performance of the two algorithms. The results showed that both algorithms performed well. The PPC had low computation time and used less control, but it also had larger position errors. The LTV-MPC had higher computation time, but smaller position errors at the cost of larger control values. The conclusion is that the MPC is preferable if computational capabilities are available. Room for future work exists in the form of comparing additional controller types for autonomous vehicles and exploring different tuning parameters for the MPC controller. The simulation could also be expanded to more accurately reflect real world conditions. / Målet med detta projekt var att jämföra två kontrollalgoritmer avsedda för att styra en självkörande bil. Jämförelsen gjordes med hjälp av en simulering som utformades i Python. Den använde sig av en algoritm som slumpmässigt satte ihop vägar från förkonstruerade delar för att skapa banor för den självkörande bilen att följa. I denna miljö har vi testat två algoritmer, en LTV-MPC och en PPC. Vi jämförde hur pass väl de följer banor som skall likna riktiga vägar, hur mycket styrning de använder sig av för att bedöma energianvändning, hur väl de förhåller sig till begränsningar på acceleration och styrning, och den beräkningstiden som krävdes för att köra vår algoritm. Datan samlades genom att köra längs med tre grupper av slumpmässigt genererade vägar med båda kontrollalgoritmerna. En grupp innehöll huvudsakligen raka sträckor, en innehöll en del svängar, och en innehöll mycket svängar. ANOVA-testet användes för att göra jämförelsen mellan resultatet av dessa två algoritmer. Resultatet visade att båda algoritmer presterar väl. PPCn hade låg beräkningstid och mindre styrvärden, men större positionsfel. MPCn hade högre beräkningstid och större styrvärden, men mindre positionsfel. Slutsatsen är att MPCn är att föredra om beräkningsmöjligheterna finns tillgängliga. Det finns utrymme för framtida arbete i form av att jämföra fler kontrollalgoritmer och att utforska fler parameter justeringar för MPCn. Utöver det finns det även utrymme för en simulation som reflekterar verkligheten noggrannare.

In recent years, mobile robots have gained tremendous attention from the entire society: the industry is aiming at selling more intelligent products while the academia is improving their performance from all perspectives. Real world examples include autnomous driving vehicles, multirotors, legged robots, etc. One of the challenging tasks commonly faced by all game players, and all robotics platforms, is to plan motion or locomotion of the robot, calculate an optimal trajectory according to certain criterion and control it accordingly. Difficulty of solving such task usually arises from high-dimensionality and complexity of the system dynamics, fast changing conditions imposed as constraints and necessity for real-time deployment. This work proposes a method over the aforementioned mission by solving an optimal control problem in a receding horizon fashion. Unlike the existing Sequential Linear Quadratic [1] algorithm which is a continuous-time variant of Differential Dynamic Programming [2], we tackle the problem in a discretized multiple shooting fashion. Sequential Quadratic Programming is employed as optimization technique to solve the constrained Nonlinear Programming iteratively. Moreover, we apply trust region method in the sub Quadratic Programming to handle potential indefiniteness of Hessian matrix as well as to improve robustness of the solver. Simulation and benchmark with previous method have been conducted on robotics platforms to show the effectiveness of our solution and superiority under certain circumstances. Experiments have demonstrated that our method is capable of generating trajectories under complicated scenarios where the Hessian matrix contains negative eigenvalues (e.g. obstacle avoidance). / De senaste åren har mobila robotar fått enorm uppmärksamhet från hela samhället: branschen siktar på att sälja mer intelligenta produkter samtidigt som akademin förbättrar sina prestationer ur alla perspektiv. Exempel på verkligheten inkluderar autonoma körande fordon, multirotorer, robotar med ben, etc. En av de utmanande uppgifterna som vanligtvis alla spelare och alla robotplattformar står inför är att planera robotens rörelse eller rörelse, beräkna en optimal bana enligt vissa kriterier och kontrollera det därefter. Svårigheter att lösa en sådan uppgift beror vanligtvis på hög dimensionalitet och komplexitet hos systemdynamiken, snabbt föränderliga villkor som åläggs som begränsningar och nödvändighet för realtidsdistribution. Detta arbete föreslår en metod över det tidigare nämnda uppdraget genom att lösa ett optimalt kontrollproblem på ett vikande horisont. Till skillnad från den befintliga Sequential Linear Quadratic [1] algoritmen som är en kontinuerlig tidsvariant av Differential Dynamic Programming [2], tar vi oss an problemet på ett diskretiserat multipelfotograferingssätt. Sekventiell kvadratisk programmering används som optimeringsteknik för att lösa den begränsade olinjära programmeringen iterativt. Dessutom tillämpar vi trust region-metoden i den sub-kvadratiska programmeringen för att hantera potentiell obestämdhet av hessisk matris samt för att förbättra lösarens robusthet. Simulering och benchmark med tidigare metod har utförts på robotplattformar för att visa effektiviteten hos vår lösning och överlägsenhet under vissa omständigheter. Experiment har visat att vår metod är kapabel att generera banor under komplicerade scenarier där den hessiska matrisen innehåller negativa egenvärden (t.ex. undvikande av hinder).

Tesis por compendio / [ES] Esta tesis está dedicada al desarrollo de conceptos de control predictivo basados en modelos para la compensación de armónicos en sistemas de potencia con fuentes de energía renovables. En concreto, estos conceptos proporcionan una corriente de compensación de referencia para un filtro activo de potencia conectado en el punto de acoplamiento común, mejorando así la calidad de potencia del sistema. No obstante, los resultados podrían aplicarse en general a problemas de control en los que el objetivo sea seguir la forma de una determinada señal. La tesis propone dos métodos principales de control basados en la teoría de control predictivo de modelos (MPC). El primer controlador, es decir, el control predictivo de modelos con conformación de señal de estado lineal (LS3MPC), se basa en la teoría MPC cuadrática estándar. Sin embargo, contrariamente a la práctica habitual de control de referencia fija, el LS3MPC incorpora la dinámica deseada del sistema directamente en su función de coste, utilizando los denominados residuos de clase de forma lineal. Este enfoque permite que la función de costes del LS3MPC sea más adaptable y ofrezca más compensaciones dinámicas, especialmente cuando está sometida a restricciones. Al utilizar los residuos de clase de forma, el problema MPC garantiza que la planta controlada siga la dinámica dada por la clase de forma. En este caso, la dinámica deseada está determinada por la clase de forma armónica lineal propuesta, es decir, la dinámica de una señal armónica fundamental de frecuencia fija. Desde el punto de vista de la implementación, se propone una formulación MPC explícita para el LS3MPC con el fin de mejorar su aplicabilidad en tiempo real. El LS3MPC explícito propuesto utiliza un enfoque de malla equidistante en formato tensorial para aproximar la solución MPC explícita con afinidad por partes. Usando la descomposición tensorial, el LS3MPC explícito puede romper la maldición de la dimensionalidad, reduciendo significativamente la carga de memoria y trivializando el problema en tiempo real de localización de puntos. El segundo controlador, es decir, el control predictivo de modelo de ciclo límite (LCMPC), se centra en resolver las deficiencias del LS3MPC. En concreto, el LCMPC aborda la falta de control directo de la amplitud recurriendo a la teoría MPC no lineal. El LCMPC introduce una clase de forma armónica no lineal basada en una forma normal de bifurcación supercrítica de Neimark-Sacker. Al igual que el LS3MPC, el LCMPC también incorpora el residual de su clase de forma armónica no lineal directamente en su función de coste, proporcionando las mismas ventajas mencionadas anteriormente. En cuanto a la estabilidad del sistema, se desarrollan condiciones suficientes, para un estado inicial predeterminado, que garanticen que el sistema de bucle cerrado permanece dentro de la región de atracción de la forma normal ante una perturbación suficientemente pequeña. Ambos controladores se someten a pruebas con estudios de simulación en múltiples escenarios, proporcionando resultados de compensación consistentemente satisfactorios. / [CA] Aquesta tesi està dedicada al desenvolupament de conceptes de control predictiu basats en models per a la compensació d'harmònics en sistemes de potència amb fonts d'energia renovables. En concret, aquests conceptes proporcionen un corrent de compensació de referència per a un filtre actiu de potència connectat en el punt d'acoblament comú, millorant així la qualitat de potència del sistema. No obstant això, els resultats podrien aplicar-se en general a problemes de control en els quals l'objectiu siga seguir la forma d'un determinat senyal. La tesi proposa dos mètodes principals de control basats en la teoria de control predictiu de models (MPC). El primer controlador, és a dir, el control predictiu de models amb conformació de senyal d'estat lineal (LS3MPC), es basa en la teoria MPC quadràtica estàndard. No obstant això, contràriament a la pràctica habitual de control de referència fixa, el LS3MPC incorpora la dinàmica desitjada del sistema directament en la seua funció de cost, utilitzant els denominats residus de classe de manera lineal. Aquest enfocament permet que la funció de costos del LS3MPC siga més adaptable i oferisca més compensacions dinàmiques, especialment quan està sotmesa a restriccions. En utilitzar els residus de classe de forma, el problema MPC garanteix que la planta controlada seguisca la dinàmica donada per la classe de forma. En aquest cas, la dinàmica desitjada està determinada per la classe de manera harmònica lineal proposta, és a dir, la dinàmica d'un senyal harmònic fonamental de freqüència fixa. Des del punt de vista de la implementació, es proposa una formulació MPC explícita per al LS3MPC amb la finalitat de millorar la seua aplicabilitat en temps real. El LS3MPC explícit proposat utilitza un enfocament de malla equidistant en format tensorial per a aproximar la solució MPC explícita amb afinitat per parts. Usant la descomposició tensorial, el LS3MPC explícit pot trencar la maledicció de la dimensionalitat, reduint significativament la càrrega de memòria i trivialitzant el problema en temps real de localització de punts. El segon controlador, és a dir, el control predictiu de model de cicle límit (LCMPC), se centra en resoldre les deficiències del LS3MPC. En concret, el LCMPC aborda la falta de control directe de l'amplitud recorrent a la teoria MPC no lineal. El LCMPC introdueix una classe de manera harmònica no lineal basada en una forma normal de bifurcació supercrítica de Neimark-Sacker. Igual que el LS3MPC, el LCMPC també incorpora el residual de la seua classe de manera harmònica no lineal directament en la seua funció de cost, proporcionant els mateixos avantatges esmentats anteriorment. Quant a l'estabilitat del sistema, es desenvolupen condicions suficients, per a un estat inicial predeterminat, que garantisquen que el sistema de bucle tancat roman dins de la regió d'atracció de la forma normal davant una pertorbació prou xicoteta. Tots dos controladors se sotmeten a proves amb estudis de simulació en múltiples escenaris, proporcionant resultats de compensació consistentment satisfactoris. / [EN] This thesis is dedicated to developing model-based predictive control concepts for harmonic compensation in power systems with renewable energy sources. Specifically, these concepts provide a reference compensation current for an active power filter connected at the point of common coupling, thereby enhancing the system's power quality. Nevertheless, results could be generically applied to control problems where the task is to follow a certain shape of a signal. The thesis proposes two main control approaches based on model predicitve control (MPC) theory. The first controller, i.e., the linear state signal shaping model predictive control (LS3MPC), relies on standard quadratic MPC theory. However, contrary to standard fixed reference control practice, the LS3MPC embeds the desired system dynamics directly into its cost function, using the so-called linear shape class residuals. This approach allows the LS3MPC' cost function to be more adaptive, providing more dynamic trade-offs, especially when constrained. By using shape class residuals, the MPC problem ensures that the controlled plant follows the desired dynamics given by the shape class. In this case, the target dynamics are given by the proposed linear harmonic shape class, i.e, the dynamics of a fundamental harmonic signal of fixed frequency. From an application perspective, an explicit MPC formulation for the LS3MPC is proposed to enhance its real-time applicability. The proposed explicit LS3MPC uses an equidistant mesh grid approach in tensor format to approximate the piecewise affine explicit MPC solution. Using tensor decomposition, the explicitLS3MPC can break the curse of dimensionality, significantly reducing memory burden and trivializing the online point localization problem. The second controller, i.e., the limit cycle model predictive control (LCMPC), focuses on addressing the shortcomings of the LS3MPC. Namely, the LCMPC addresses the lack of direct amplitude control by reaching into nonlinear MPC theory. The LCMPC introduces a nonlinear harmonic shape class based on a supercritical Neimark-Sacker bifurcation normal form. Similarly to theLS3MPC, the LCMPC also embeds its nonlinear harmonic shape class residual directly in its cost function, providing the same benefits mentioned before. Regarding system stability, sufficient conditions are developed for a given initial state to ensure that the closed-loop system remains inside the normal form region of attraction for a sufficiently small disturbance. Both controllers are tested with simulation studies in multiple scenarios, providing consistently satisfactory compensation results. / The contributions on this doctoral thesis were partly developed at the Fraunhofer Institute for Silicon Technology ISIT within the project North German Energy Transition 4.0 (ger- man: Norddeutsche EnergieWende, NEW 4.0), which is funded by the German Federal Ministry for Economic Affairs and Energy (german: Bundesministerium für Wirtschaft und Energie, BMWi. This work was also partly funded with the project Northern German Living Lab (german: Norddeutsches Reallabor, NRL) by the Federal Ministry for Economic Affairs and Climate Action, by Generalitat Valenciana regional government through project CIAICO/2021/064, and by the Free and Hanseatic City of Hamburg (Hamburg City Parliament publication 20/11568). / Cateriano Yáñez, C. (2024). State Shaping Model Predictive Control for Harmonic Compensation [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/209406 / Compendio

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systems.

Les exigences croissantes sur l'efficacité énergétique des bâtiments, l'évolution du {marché} énergétique, le développement technique récent ainsi que les particularités du poste de chauffage ont fait du MPC le meilleur candidat pour la régulation thermique des bâtiments à occupation intermittente. Cette thèse présente une méthodologie basée sur la commande prédictive distribuée visant un compromis entre l'optimalité, la simplicité et la flexibilité de l'implantation de la solution proposée. Le développement de l'approche est progressif : à partir du cas d'une seule zone, la démarche est ensuite étendue au cas multizone et / ou multisource, avec la prise en compte des couplages thermiques entre les zones adjacentes. Après une formulation quadratique du critère MPC pour mieux satisfaire les objectifs économiques du contrôle, la formulation linéaire est retenue. Pour répartir la charge de calcul, des méthodes de décomposition linéaire (comme Dantzig-Wolfe et Benders) sont employées. L'efficacité des algorithmes distribués proposés est illustrée par diverses simulations. / The increasing requirements on energy efficiency of buildings, the evolution of the energy market, the technical developments and the characteristics of the heating systems made of MPC the best candidate for thermal control of intermittently occupied buildings. This thesis presents a methodology based on distributed model predictive control, aiming a compromise between optimality, on the one hand, and simplicity and flexibility of the implementation of the proposed solution, on the other hand. The development of the approach is gradually. The mono-zone case is initially considered, then the basic ideas of the solution are extended to the multi-zone and / or multi-source case, including the thermal coupling between adjacent zones. Firstly we consider the quadratic formulation of the MPC cost function, then we pass towards a linear criterion, in order to better satisfy the economic control objectives. Thus, linear decomposition methods (such as Dantzig-Wolfe and Benders) represent the mathematical tools used to distribute the computational charge among the local controllers. The efficiency of the distributed algorithms is illustrated by simulations.