Colloidal particles provide the ideal building blocks for the next generation of microdevices, such as advanced sensors and precision drug delivery systems. However, many such applications require the use of top-down (i.e. humanly controllable) forces to manipulate colloidal particles with single-particle precision, and current methods can only achieve such precision for small numbers of particles at a time. To address this challenge, we propose using chemical forces in combination with existing top-down techniques to enable the control of larger numbers of particles simultaneously. Controlling colloids using chemical reactions is a novel technique not typically utilized. Due to its distinct difference from other control methods, it provides new degrees of freedom to work with which offer new opportunities for designing colloidal devices. In this dissertation, we show how modern control theory can be used to implement the control of colloidal particles using chemical forces. We use Brownian dynamics simulations to test control strategies for three different situations: directly controlling chemical reactions to produce a desired concentration gradient, controlling a reactive colloidal particle that interacts chemically with other colloids to move them to desired locations, and controlling the dynamics of active colloidal particles to manipulate their collective behavior. The results obtained in this work will demonstrate the plausibility of each of these three control strategies and provide insights into the choices of physical parameters that can be used in future experiments.

L'évolution de l'électronique de puissance depuis ces dernières années est le résultat des enjeux énergétiques actuels qui exigent, entre autres, des architectures de conversion d'énergie capables de traiter des puissances de plus en plus importantes. Parmi les éléments les plus caractéristiques de cette évolution, l'avancement technologique des composants semi-conducteurs (nouveaux composants SiC ou GaN) ainsi que la conception de nouvelles architectures de convertisseurs statiques jouent un rôle important. Parmi ces architectures, différentes associations basées sur la connexion en série et en parallèle de cellules de commutation classiques ont été proposées. Ces associations permettent d'augmenter la puissance traitée par les convertisseurs sans accroitre les contraintes au niveau des interrupteurs. Elles permettent également l'obtention de signaux de sortie d'une meilleure qualité avec des fréquences apparentes de découpage plus importantes. Ces architectures utilisent des éléments de stockage d'énergie qui diminuent les contraintes au niveau des interrupteurs mais qui exigent, en revanche, une régulation précise des grandeurs de tension ou de courant propres à ces éléments. Pour l'association en série, les tensions des condensateurs doivent rester autour d'une fraction de la tension du bus d'entrée. Pour l'association en parallèle, le courant de sortie doit être réparti équitablement entre les différents bras afin d'éviter les phénomènes non linéaires propres aux éléments magnétiques utilisés dans les inductances (séparées ou magnétiquement couplées). Dans la première partie de cette thèse, nous présentons les généralités de l'association en série et parallèle des cellules de commutation. La modélisation des éléments magnétiques utilisés pour la mise en parallèle est également détaillée dans le but d'identifier de possibles sources de déséquilibre sur la répartition du courant de sortie. Une modélisation matricielle est appliquée pour simplifier la relation entre les variables propres à chaque association et les ordres de commande de toutes les cellules. Cette modélisation matricielle sera la base des stratégies de commande que nous avons développées dans la suite de nos travaux. Dans la deuxième partie de cette thèse, nous présentons les différentes stratégies de commande pouvant être appliquées sur ces convertisseurs. Les premières stratégies sont basées sur une approche classique utilisant un modulateur, un générateur d'ordres de commande et des régulateurs de type linéaire pour la régulation des variables internes et externes de chaque association. En termes de modulateurs, nous présentons principalement un modulateur de type PS (Phase Shifted), tandis que quelques applications et résultats sont présentés pour un modulateur de type PD (Phase Disposition). D'autres stratégies basées sur la commande prédictive sont également présentées. La première est la stratégie MPC qui utilise une fonction de coût pour choisir l'état optimal du convertisseur pour chaque période d'échantillonnage. Cette stratégie a été introduite récemment dans le domaine des convertisseurs statiques et présente des avantages liées à la facilité de sa mise en place ainsi qu'aux réponses du système lors des régimes transitoires. La deuxième stratégie, basée sur la commande prédictive, utilise des instants de commutation variables, une fonction de coût simplifiée et une machine d'état. Cette dernière permet de gérer les ordres de commande de toutes les cellules de commutation en fonction des variables à réguler. En plus des avantages liés à la stratégie MPC, sa mise en place est bien plus simple car elle fonctionne à une fréquence de découpage fixe et s'adapte facilement à différents points de fonctionnement. Dans la dernière partie de cette thèse, nous présentons l'implantation expérimentale de ces stratégies afin de valider leur performance sur les convertisseurs multicellulaires. / In the last years, the development in the power electronics field is the result of the current energy challenges. These challenges require power converters able to work with increasingly important powers. Among the most characteristic elements of this development, we can find the technological advancements of the semiconductor devices (based principally in SiC and GaN) and the conception of new power converters topologies. These new power converter topologies are principally based on the serial and parallel association of classical commutation cells. With these associations, the energy treated by the converter can be increased using the current semiconductor technology. The quality of the output signals can also be improved with higher apparent switching frequencies. These associations use elements for storing energy, such as inductors or capacitors. They equally allow the reduction of the constraints on the switches given the higher voltages and currents. However, the use of these elements requires a good control of the capacitors' voltage for the serial connection and a good distribution of the output current among the different phases for the parallel connection. In the parallel connection, when we use Inter Cells Transformers (ICT) instead of classical inductors, all the phase currents reduce their ripples while their frequency is reduced. Nevertheless, some differences between all the phases' currents can entail non-linear phenomena, producing perturbations and instabilities in the system. In the serial connection, the capacitor voltages must stay around a fraction of the input voltage in order to get an optimal multilevel output voltage. In the first part of this thesis, we present the generalities of the serial and parallel association of classical commutation cells. Different models of magnetic elements are used for getting a better representation of an ICT; these models are used for finding possible sources of currents imbalances. A matrix model is used to simplify the relationship between the control variables with the control of each commutation cell. In the second part of this thesis, some control strategies that can be applied with these converters are presented. The first strategy is based on a conventional approach that uses a modulator, a state machine for generating the commands of each cell and linear regulators for controlling the internal and external variables (output voltage and currents, capacitors in the serial association and the distribution of the current for the parallel connection). In terms of modulators, we present primarily a PS (Phase Shifted) modulator while some applications and results are presented for a PD (Phase Disposition) modulator. Other strategies based on predictive control are also presented. The first of these strategies is the classical MPC (model predictive control) strategy that uses a cost function to select the optimal state of the converter for each sampling period. This strategy has recently been introduced in the field of static converters and it has several advantages related to the facility of its implementation and the optimal transient responses. The second strategy uses variable switching instants, a simplified cost function and a state machine. The state machine is used to manage the capacitors' voltages and the differential currents (differences between the phase currents) while the cost function is used for controlling the output voltage and current. This strategy is simpler to be implemented, presents fast transient responses and works with a fixed switching frequency in different operating points. In the last part of this thesis, we present the experimental implementation of these strategies in order to validate their performance in the power converters based in the serial and parallel association of classical commutation cells.

Made available in DSpace on 2014-12-17T14:56:17Z (GMT). No. of bitstreams: 1 GasparFDJ_DISSERT.pdf: 3388304 bytes, checksum: 086a8f61099f69978a8b9f477f351d24 (MD5) Previous issue date: 2014-01-23 / Petr?leo Brasileiro SA - PETROBRAS / Slugging is a well-known slugging phenomenon in multiphase flow, which may cause problems such as vibration in pipeline and high liquid level in the separator. It can be classified according to the place of its occurrence. The most severe, known as slugging in the riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it is capable of causing severe pressure fluctuations in the flow of the process, excessive vibration, flooding in separator tanks, limited production, nonscheduled stop of production, among other negative aspects that motivated the production of this work . A feasible solution to deal with this problem would be to design an effective method for the removal or reduction of the system, a controller. According to the literature, a conventional PID controller did not produce good results due to the high degree of nonlinearity of the process, fueling the development of advanced control techniques. Among these, the model predictive controller (MPC), where the control action results from the solution of an optimization problem, it is robust, can incorporate physical and /or security constraints. The objective of this work is to apply a non-conventional non-linear model predictive control technique to severe slugging, where the amount of liquid mass in the riser is controlled by the production valve and, indirectly, the oscillation of flow and pressure is suppressed, while looking for environmental and economic benefits. The proposed strategy is based on the use of the model linear approximations and repeatedly solving of a quadratic optimization problem, providing solutions that improve at each iteration. In the event where the convergence of this algorithm is satisfied, the predicted values of the process variables are the same as to those obtained by the original nonlinear model, ensuring that the constraints are satisfied for them along the prediction horizon. A mathematical model recently published in the literature, capable of representing characteristics of severe slugging in a real oil well, is used both for simulation and for the project of the proposed controller, whose performance is compared to a linear MPC / A golfada ? um regime inst?vel do fluxo multif?sico, com oscila??es de press?o e vaz?o abruptas no processo de produ??o de petr?leo, podendo ocasionar problemas tais como vibra??o na tubula??o e alto n?vel de l?quido nos separadores. Pode ser classificada de acordo com seu local de ocorr?ncia. A mais severa destas, conhecida como golfada no riser, ocorre na tubula??o vertical que alimenta a plataforma. Conhecida tamb?m como golfada severa, ela ? capaz de causar bruscas oscila??es na press?o, nas vaz?es do processo, vibra??o excessiva, inunda??o dos tanques separadores, produ??o limitada, parada n?o programada da plataforma, entre outros aspectos negativos que motivaram a produ??o deste trabalho. Uma solu??o vi?vel para lidar com tal problema seria projetar um m?todo efetivo para a remo??o ou diminui??o deste regime, como um controlador. De acordo com a literatura, o controlador convencional PID n?o apresenta bons resultados devido ao alto grau de n?o linearidade do processo, o que impulsionou o desenvolvimento de t?cnicas avan?adas de controle. Dentre estas, o controlador preditivo, cuja a??o de controle resulta da solu??o de um problema de otimiza??o, al?m de ser uma t?cnica que apresenta robustez e pode incorporar restri??es f?sicas e/ou de seguran?a. O objetivo deste trabalho ? estudar a aplica??o de uma t?cnica de controle preditivo n?o linear ao controle de golfada severa, visando controlar a quantidade de massa l?quida no riser atuando na v?lvula de produ??o e, indiretamente, suprimir as oscila??es de vaz?o e press?o. Com a finalidade de obter benef?cios ambientais e econ?micos. A t?cnica de controle preditivo proposta baseia-se no uso de aproxima??es lineares do modelo e na resolu??o repetida de um problema de otimiza??o quadr?tica que proporciona solu??es que melhoram a cada itera??o. No caso em que a converg?ncia desse algoritmo ? satisfeita, os valores preditos das vari?veis do processo s?o iguais ?queles que seriam obtidos pelo modelo n?o linear original, garantindo que as restri??es nessas vari?veis sejam satisfeitas ao longo do horizonte de predi??o. Um modelo matem?tico publicado recentemente na literatura, capaz de representar caracter?sticas da golfada severa em um po?o real, ? utilizado tanto para a simula??o, quanto para projeto do controlador proposto, cujo desempenho ? comparado ao de um controlador preditivo linear

To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank.

Semitrailer trucks are widely used for transportation of goods in Sweden and around the world. Given their usefulness, and since they require specialized drivers, there is an increased need to automate the operation of these vehicles. In particular, reversing these vehicles is considered a challenging maneuver, mainly because of the jackknifing effect. To tackle this challenge, this thesis investigates path following for reversing single-joint semitrailer trucks, by comparing two path-following controllers, corresponding to a Linear Quadratic Regulator (LQR) and a Model Predictive Control (MPC), respectively. Both controllers receive kinematically feasible reference trajectories from a path planner (which is part of another thesis work), which makes it possible to avoid jackknifing as long as the reference joint angle between the trailer and the truck is closely followed. Moreover, they use a linearized and discretized 1-trailer kinematic model, defined in terms of the reference tracking errors for the truck as states. To evaluate the performance of the controllers, a Python simulation is implemented using the 1-trailer kinematic model. Using this simulation, the controllers are compared using metrics related to the reference tracking errors along the generated path and the controller execution time. The results show that the LQR and the MPC controllers perform similarly for most cases. Even though there are certain cases where the MPC outperforms the LQR, the execution time of the MPC is at least one order of magnitude slower, which makes the LQR an attractive solution for practical implementations, as long as certain assumptions (small initial deviations, reliable measurements) are ensured. As such, an LQR controller might be preferred by the industry because, while the performance from both controllers is similar, it can be considered a more efficient controller. / Lastbilar med olika släpvagnskombinationer används ofta för godstransporter i Sverige och runt om i världen. Med tanke på deras användbarhet och eftersom de kräver specialiserade förare finns det ett ökat behov av att automatisera driften av dessa fordon. I synnerhet anses backning av dessa fordon vara en utmanande manöver, främst på grund av jackknifseffekten. För att lösa detta problem undersöker denna rapport vägföljande för backande lastbilar med släp genom att jämföra två olika vägföljande styrenheter: Linear Quadtratic Regulator (LQR) och Model Predictive Control (MPC). Båda styrenheterna får kinematiskt genomförbara referensbanor från en vägplanerare (som är en del av en annan uppsats), vilket gör det möjligt att undvika jackknipning så länge referensvinkeln mellan släpet och lastbilen följs noggrant. Dessutom använder de en linjäriserad och diskretiserad kinematisk modell med en lastbil, definierad i termer av lastbilens referensspårningsfel som tillstånd. För att utvärdera kontrollernas prestanda implementeras en Python-simulering med den kinematisk modell med en lastbil. Med denna simulering jämförs de två styrenheterna med mått relaterade till referensspårningsfelen längs den generarade vägen och styrenheternas exekveringstid. Resultaten visar att LQR och MPCpresterar likadant i de flesta fallen. Även om det finns vissa fall där MPC överträffar LQR, är exekveringstiden för MPC åtminstone en storleksordning långsammare, vilket gör LQR till en attraktiv lösning för praktiska implementeringar, så länge som vissa antaganden (små initiala avvikelser, pålitliga mått) säkerställs. Som sådan kan en LQR-styrenhet föredras av industrin, för även om prestandan från båda styrenheterna är lika, kan den betraktas som en enklare styrenhet.

Distributed coordination of multi-agent systems (MASs) has been widely studied in various emerging engineering applications, including connected vehicles, wireless networks, smart grids, and cyber-physical systems. In these contexts, agents make the decision locally, relying on the interaction with their immediate neighbors over the connected communication networks. The study of distributed coordination for the multi-agent system (MAS) with constraints is significant yet challenging, especially in terms of ubiquitous uncertainties, the heavy communication burden, and communication delays, to name a few. Hence, it is desirable to develop distributed algorithms for the constrained MAS with these practical issues. In this dissertation, we develop the theoretical results on robust distributed model predictive control (DMPC) algorithms for two types of control problems (i.e., formation stabilization problem and consensus problem) of the constrained and uncertain MAS and apply robust DMPC algorithms in applications of cooperative marine vehicles. More precisely, Chapter 1 provides a systematic literature review, where the state-of-the-art DMPC for formation stabilization and consensus, robust MPC, and MPC for motion control of marine vehicles are introduced. Chapter 2 introduces some notations, necessary definitions, and some preliminaries. In Chapter 3, we study the formation stabilization problem of the nonlinear constrained MAS with un- certainties and bounded time-varying communication delays. We develop a min-max DMPC algorithm with the self-triggered mechanism, which significantly reduces the communication burden while ensuring closed-loop stability and robustness. Chapter 4 investigates the consensus problem of the general linear MAS with input constraints and bounded time-varying delays. We design a robust DMPC-based consensus protocol that integrates a predesigned consensus protocol with online DMPC optimization techniques. Under mild technical assumptions, the estimation errors propagated over prediction due to delay-induced inaccurate neighboring information are proved bounded, based on which a robust DMPC strategy is deliberately designed to achieve robust consensus while satisfying control input constraints. Chapter 5 proposes a Lyapunov-based DMPC approach for the formation tracking control problem of co-operative autonomous underwater vehicles (AUVs) subject to environmental disturbances. A stability constraint leveraging the extended state observer-based auxiliary control law and the associated Lyapunov function is incorporated into the optimization problem to enforce the stability and enhance formation tracking performance. A collision-avoidance cost is designed and employed in the DMPC optimization problem to further guarantee the safety of AUVs. Chapter 6 presents a tube-based DMPC approach for the platoon control problem of a group of heterogeneous autonomous surface vehicles (ASVs) with input constraints and disturbances. In particular, a coupled inter-vehicle safety constraint is added to the DMPC optimization problem; it ensures that neighboring ASVs maintain the safe distance and avoid inter-vehicle collision. Finally, we summarize the main results of this dissertation and discuss some potential directions for future research in Chapter 7. / Graduate / 2023-04-19

The automotive industry has been involved in making vehicles autonomous to different levels in the past decade rapidly. Particularly in the commercial vehicle market, there is a significant necessity to make trucks have a certain level of automation to help reduce dependence on human efforts to drive. This could help in reducing several accidents caused by human error. Interestingly there are several challenges and solutions in achieving and implementing autonomous driving for trucks. First, a benchmark of different control architectures that can make a truck drive autonomously are explored. The chosen controllers (Pure Pursuit, Stanley, Linear Quadratic Regulator, Sliding Mode Control and Model Predictive Control) vary in their simplicity in implementation and versatility in handling different vehicle parameters and constraints. A thorough comparison of these path tracking controllers are performed using several metrics. Second, a collision avoidance system based on cubic polynomials, inspired by rapidly exploring random tree (RRT) is presented. Some of the path tracking controllers are limited by their ability and hence a standalone collision avoidance system is needed to provide safe maneuvering. Simulations are performed for different test cases with and without obstacles. These simulations help compare safety margin and driving comfort of each path tracking controller that are integrated with the collision avoidance system. Third, different performance metrics like change in acceleration input, change in steering input, error in path tracking, deviation from base frame of track file and lateral and longitudinal margin between ego and target vehicle are presented. To conclude, a set of suitable controllers for heavy articulated vehicles are developed and benchmarked. / Bilindustrin har varit involverad i att göra fordon autonoma till olika nivåer under det senaste decenniet snabbt. Särskilt på marknaden för kommersiella fordon finns det ett stort behov av att få lastbilar att ha en viss nivå av automatisering för att minska beroendet av mänskliga ansträngningar att köra. Detta kan hjälpa till att minska flera olyckor orsakade av mänskliga fel. Intressant nog finns det flera utmaningar och lösningar för att uppnå och implementera autonom körning för lastbilar. Först utforskas ett riktmärke av olika styrarkitekturer som kan få en lastbil att köra autonomt. De valda kontrollerna (Pure Pursuit, Stanley, Linear Quadratic Regulator, Sliding Mode Control och Model Predictive Control) varierar i sin enkelhet i implementering och mångsidighet när det gäller att hantera olika fordonsparametrar och begränsningar. En grundlig jämförelse av dessa vägspårningskontroller utförs med hjälp av flera mätvärden. För det andra presenteras ett system för undvikande av kollisioner baserat på kubiska polynom, inspirerat av snabbt utforskande slumpmässiga träd (RRT). Vissa av vägspårningskontrollerna är begränsade av sin förmåga och därför behövs ett fristående system för att undvika kollisioner för att ge säker manövrering. Simuleringar utförs för olika testfall med och utan hinder. Dessa simuleringar hjälper till att jämföra säkerhetsmarginal och körkomfort för varje vägspårningskontroller som är integrerade med kollisionsundvikande systemet. För det tredje presenteras olika prestandamått som förändring i accelerationsinmatning, förändring i styrinmatning, fel i banspårning, avvikelse från basramen för spårfilen och lateral och longitudinell marginal mellan ego och målfordon. Avslutningsvis utvecklas och benchmarkas en uppsättning lämpliga styrenheter för tunga ledade fordon.

The thesis focuses on the design of a control strategy for safety-critical remote teleoperation. The main goal is to make the controlled system track the desired velocity specified by a human operator while avoiding obstacles despite communication delays. Different methods adopting Control Barrier Functions (CBFs) and Model Predictive Control (MPC) have been explored and tested. In this combination, CBFs are used to define the safety constraints the system has to respect to avoid obstacles, while MPC provides the framework for filtering the desired input by solving an optimization problem. The resulting input is sent to the remote system, where appropriate low-level velocity controllers translate it into system-specific commands. The main novelty of the thesis is a method to make the CBFs robust against the uncertainties affecting the system’s state due to network delays. Other techniques are investigated to improve the quality of the system information starting from the delayed one and to formulate the optimization problem without knowing the specific dynamic of the controlled system. The results show how the proposed method successfully solves the safetycritical teleoperation problem, making the controlled systems avoid obstacles with different types of network delay. The controller has also been tested in simulation and on a real manipulator, demonstrating its general applicability when reliable low-level velocity controllers are available. / Avhandlingen fokuserar på utformningen av en kontrollstrategi för säkerhetskritisk fjärrstyrd teleoperation. Huvudmålet är att få det kontrollerade systemet att följa den önskade hastigheten som specificeras av en mänsklig operatör samtidigt som hinder undviks trots kommunikationsfördröjningar. Olika metoder som använder Control Barrier Functions (CBFs) och Model Predictive Control har undersökts och testats. I denna kombination används CBFs för att definiera de säkerhetsbegränsningar som systemet måste respektera för att undvika hinder, medan MPC utgör ramverket för filtrering av den önskade indata genom att lösa ett optimeringsproblem. Den resulterande indata skickas till fjärrsystemet, där lämpliga hastighetsregulatorer på låg nivå översätter den till systemspecifika kommandon. Den viktigaste nyheten i avhandlingen är en metod för att göra CBFs robust mot de osäkerheter som påverkar systemets tillstånd på grund av nätverksfördröjningar. Andra tekniker undersöks för att förbättra kvaliteten på systeminformationen med utgångspunkt från den fördröjda informationen och för att formulera optimeringsproblemet utan att känna till det kontrollerade systemets specifika dynamik. Resultaten visar hur den föreslagna metoden framgångsrikt löser det säkerhetskritiska teleoperationsproblemet, vilket gör att de kontrollerade systemen undviker hinder med olika typer av nätverksfördröjningar. Styrningen har också testats i simulering och på en verklig manipulator, vilket visar dess allmänna tillämpbarhet när tillförlitliga lågnivåhastighetsregulatorer finns tillgängliga.

Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption. Optimal control of multiple energy buffers within the vehicle proves aneffective approach for reducing energy consumption. Energy is temporarilystored in a buffer when its cost is small and released when it is relativelyexpensive. An example of an energy buffer is the vehicle body. Before goingup a hill, the vehicle can accelerate to increase its kinetic energy, which canthen be consumed on the uphill stretch to reduce the engine load. The simplestrategy proves effective for reducing fuel consumption. The thesis generalizes the energy buffer concept to various vehicular componentswith distinct physical disciplines so that they share the same modelstructure reflecting energy flow. The thesis furthermore improves widely appliedcontrol methods and apply them to new applications. The contribution of the thesis can be summarized as follows: • Developing a new function to make the equivalent consumption minimizationstrategy (ECMS) controller (which is one of the well-knownoptimal energy management methods in hybrid electric vehicles (HEVs))more robust. • Developing an integrated controller to optimize torque split and gearnumber simultaneously for both reducing fuel consumption and improvingdrivability of HEVs. • Developing a one-step prediction control method for improving the gearchanging decision. • Studying the potential fuel efficiency improvement of using electromechanicalbrake (EMB) on a hybrid electric city bus. • Evaluating the potential improvement of fuel economy of the electricallyactuated engine cooling system through the off-line global optimizationmethod. • Developing a linear time variant model predictive controller (LTV-MPC)for the real-time control of the electric engine cooling system of heavytrucks and implementing it on a real truck. / <p>QC 20160128</p>

<p>Nonlinear systems occur in industrial processes, economical systems, biotechnology and in many other areas. The thesis treats methods for system identification and control of such nonlinear systems, and applies the proposed methods to a solar heating/cooling plant. </p><p>Two applications, an anaerobic digestion process and a domestic solar heating system are first used to illustrate properties of an existing nonlinear recursive prediction error identification algorithm. In both cases, the accuracy of the obtained nonlinear black-box models are comparable to the results of application specific grey-box models. Next a convergence analysis is performed, where conditions for convergence are formulated. The results, together with the examples, indicate the need of a method for providing initial parameters for the nonlinear prediction error algorithm. Such a method is then suggested and shown to increase the usefulness of the prediction error algorithm, significantly decreasing the risk for convergence to suboptimal minimum points. </p><p>Next, the thesis treats model based control of systems with input signal dependent time delays. The approach taken is to develop a controller for systems with constant time delays, and embed it by input signal dependent resampling; the resampling acting as an interface between the system and the controller.</p><p>Finally a solar collector field for combined cooling and heating of office buildings is used to illustrate the system identification and control strategies discussed earlier in the thesis, the control objective being to control the solar collector output temperature. The system has nonlinear dynamic behavior and large flow dependent time delays. The simulated evaluation using measured disturbances confirm that the controller works as intended. A significant reduction of the impact of variations in solar radiation on the collector outlet temperature is achieved, though the limited control range of the system itself prevents full exploitation of the proposed feedforward control. The methods and results contribute to a better utilization of solar power.</p>