Privacy is a crucial aspect of any system involving user-supplied data. There exist multiple approaches to protecting the identity and secrecy of users in data submission systems. In this thesis, I consider the case of privacy-enhancing of data reporting in Participatory Sensing systems. I conducted an extensive literature overview to explore privacy-oriented enhancements to data submission that are applicable in the PS systems. I designed a protocol for proximity-based data aggregation that utilizes Multi-party Secure Computations over Bluetooth Low Energy. Users are divided into groups that perform sub-aggregations and report results to central entities, protecting themselves from honest-but-curious adversary threats. I present a mobile app and web servers for central entities that follow the design of the protocol. I evaluated the achieved effectiveness and discuss the utility and privacy trade-offs. The implementation performs typically for an MPC system with high communication overhead, and is implemented over Bluetooth, with the additional time needed for discovering and connecting devices. The overall performance of the system suggests that deployments targeting 1-second intervals of data submission are feasible. Main use cases are sensitive measurements, such as medical data or highly private user information. / Sekretess är en avgörande aspekt av alla system som involverar data som tillhandahålls av användare. Det finns flera tillvägagångssätt för att skydda användarnas identitet och sekretess i datainlämningssystem. I den här avhandlingen behandlar jag fallet med integritetsförbättrande datarapportering i Participatory Sensing-system. Jag genomförde en omfattande litteraturöversikt för att utforska integritetsorienterade förbättringar av datainlämning som är tillämpliga i PS-systemen. Jag designade ett protokoll för närhetsbaserad dataaggregering som använder flerpartssäkra beräkningar över Bluetooth Low Energy. Användare är indelade i grupper som utför sub-aggregeringar och rapporterar resultat till centrala enheter, och skyddar sig själva från ärliga men nyfikna motståndarhot. Jag presenterar en mobilapp och webbservrar för centrala enheter som följer protokollets design. Jag utvärderade den uppnådda effektiviteten och diskuterade nytta och sekretessavvägningar. Implementeringen fungerar som man kan förvänta sig för ett MPC-system med höga kommunikationskostnader, och implementeras över Bluetooth, med den extra tid som krävs för att upptäcka och ansluta enheter. Systemets övergripande prestanda tyder på att implementeringar som är inriktade på 1-sekunds intervaller för datainlämning är genomförbara. Huvudsakliga användningsfall är känsliga mätningar, såsom medicinska data eller mycket privat användarinformation.

The inherited rotation in a caster wheel allows movement in any direction, but pays at the expense of reaction torques. When implemented in a mobile robot, these forces have a negative impact in its performance. One approach is to restrict rotations on the spot by attaching a filter to the output of the motion planner. However, this formulation compromises the navigation’s completion in critical scenarios, such as parking, taking curves in narrow corridors or navigating at the presence of a high density of obstacles. Therefore, in this thesis we consider the influence of caster wheels in the motion planning stage, commonly presented as local planning. This work proposes a Model Predictive Control (MPC) based local planner that integrates the caster wheel physics into the motion planning stage. A caster wheel aware term is combined with a reference tracking based navigation, which leads to the formulation of the Caster Wheel Aware Local Planner (CWAWLP). Since this method requires knowing the caster wheel’s state and there is no sensor that provides this information, a caster wheel state observer is also formulated. In order to evaluate the impact of the caster wheel aware term, CWAWLP is compared to a Caster Wheel based Agnostic Local Planner (CWAGLP) and a Caster Wheel based Agnostic Planner Local Planner with Path Filter (CWPFLP). After running simulations for three case studies in a virtual framework, two experimental case studies are conducted in an intra-logistics robot. These are evaluated according to the navigation’s quality, motor torque usage and energy consumption. According to the patterns observed in the evaluation, CWAWLP covers a longer distance than CWAGLP wihout decreasing the navigation’s quality. At the same time, its motor torques are similar to the ones of CWPFLP. Therefore, CWAWLP is capable of considering caster wheel physics without sacrificing navigation capabilities. The formulated caster wheel aware term is compatible with any MPC based navigation algorithm and inherits the derivation of an observer capable of estimating caster wheel rotation angles and rolling speeds. Even if the caster wheel awareness has been implemented in a differential driven robot, this approach is also applicable to vehicles with an alternative drivetrain, such as car-like robots. / Den ärvda rotationen i ett hjul möjliggör rörelse i vilken riktning som helst, men fås på bekostnad av reaktionsmoment. När de implementeras i en mobil robot har dessa krafter en negativ inverkan på dess prestanda. Ett tillvägagångssätt är att begränsa rotationer på plats genom att applicera ett filter på rörelseplannerns utgång. Denna formulering komprometterar dock navigeringens slutförande i kritiska scenarier, såsom parkering, kurvor i smala korridorer eller navigering i närheten av höga hinder. Därför beaktar vi i denna avhandling påverkan av hjul på hjulplaneringen, som ofta presenteras som lokal planering. Detta arbete föreslår en Model Predictive Control (MPC) -baserad lokal planerare som integrerar svängbara länkhjuls fysik i rörelseplaneringsstadiet. En kugghjulmedveten term kombineras med en referensspårningsbaserad navigering, vilket leder till formuleringen av Caster Wheel Aware Local Planner (CWAWLP). Eftersom denna metod kräver kunskap om svängbara länkhjuls tillstånd och det inte finns någon sensor som ger denna information, formuleras också en hjulhjulstillståndsobservatör. För att utvärdera effekten av det medvetna begreppet svängbara änkhjul jämförs CWAWLP med en Caster Wheel-baserad Agnostic Local Planner (CWAGLP) och en Caster Wheel-baserad Agnostic Planner Local Planner with Path Filter (CWPFLP). Efter att ha kört simuleringar för tre fallstudier i ett virtuellt ramverk genomförs två experimentella fallstudier i en intra-logistikrobot. Dessa utvärderas enligt navigeringens kvalitet, vridmomentanvändning och energiförbrukning. Enligt de mönster som observerats i utvärderingen når CWAWLP ett längre avstånd än CWAGLP utan att sänka navigeringens kvalitet. Samtidigt liknar motorns vridmoment dem som CWPFLP. Därför kan CWAWLP ta hänsyn till svängbara länkhjuls fysik utan att offra navigationsfunktionerna. Den formulerade medhjulningsmedveten termen är kompatibel med vilken MPC-baserad navigationsalgoritm som helst och ärver härledningen av en observatör som kan uppskatta hjulets rotationsvinklar och rullningshastigheter. Även om hjulhjälpmedvetenheten har implementerats i en differentierad robot, är detta tillvägagångssätt också tillämpligt på fordon med ett alternativt drivsystem, såsom billiknande robotar.

Critical automated maneuvers for ships typically require a redundant set of thrusters. The motion control system hierarchy is commonly separated into several layers using a high-level motion controller and a thruster allocation (TA) algorithm. This allows for a modular design of the software where the high-level controller can be designed without comprehensive information on the thrusters, while detailed issues such as input saturation and rate limits are handled by the TA. However, for a certain set of thruster configurations this decoupling may result in poor control performance due to the limited knowledge in the high-level controller about the physical limitations of the ship and the behavior of the TA. This thesis investigates different approaches of improving the control performance, using nonlinear Model Predictive Control (MPC) as a foundation for the developed motion controllers due to its optimized solution and capability of satisfying constraints. First, a decoupled system is implemented and results are provided for two simple motion tasks showing problems related to the decoupling. Thereafter, two different approaches are taken to remedy the observed drawbacks. A nonlinear MPC controller is developed combining the motion controller and thruster allocation resulting in a more robust control system. Then, in order to keep the control system modularized, an investigation of possible ways to augment the decoupled system so as to achieve similar performance as the combined system is carried out. One proposed solution is a nonlinear MPC controller with time-varying constraints accounting for the current limitations of the thruster system. However, this did not always improve the control performance since the behavior of the TA still is unknown to the MPC controller.

Le travail présenté dans ce mémoire de thèse porte sur la tolérance aux défauts dans le cas des systèmes linéaires. Les moyens de communication numériques sont utilisés dans le cadre de la mise en oeuvre d'une architecture de commande tolérante aux défauts pour des systèmes complexes. Une coopération entre les modules de commande/diagnostic assure la tolérance à certains types de défauts qui affectent le système. La commande des systèmes est traditionnellement réalisée à partir d'un calculateur central qui collecte l'ensemble des informations relevées sur le procédé, puis les traite pour élaborer un ensemble de commande qui est appliqué au procédé. Avec le développement des systèmes commandés en réseaux (Networked Control System) et des systèmes embarqués, l'architecture des systèmes s'oriente vers une distribution des algorithmes de commande et de diagnostic. On se propose d'aborder le problème de la conception des stratégies de distribution de diagnostic/commande et de coopération des tâches de commande entre les sous-contrôleurs associés à chaque sous-système qui composent le système complexe et de prendre en compte les défauts des actionneurs et de capteurs affectant les sous-systèmes. Il s'agit alors d'élaborer une stratégie de commande coopérative visant à compenser les effets des défauts affectant le système. Les commandes locales sont des commandes prédictives à base de modèle (MPC: Model Predictive Control). Une analyse de stabilité a été faite en prenant en considération la défaillance du réseau de communication. / The work presented in this memory of thesis focuses on fault tolerance in the case of linear systems. Digital communication tools are used in the context of the implementation of an architecture for fault tolerant control of complex systems. A cooperation between the control/diagnosis blocks ensures the tolerance to certain types of faults which affect the system. Control systems is traditionally carried out starting from a central computer that collects all information gathered on the process. Then, these information are treated in order to develop a set of command which is applied to the process. Thanks to the development of the Networked System Control and embedded systems, systems architecture is oriented towards a distributed control and diagnostic algorithms. One proposes to address the problem of designing distribution strategies for diagnosis/control and control tasks cooperation between sub-controllers associated at each subsystem comprising the complex system and to take into account the faults on the actuators and sensors that affect the subsystems. Then a cooperative control strategy is proposed. It aims at compensating the effects of the faults affecting the system. Local controls are based on Model Predictive Control (MPC). An analysis of stability was made taking into account the failure of the communication network

Une utilisation efficace et coordonnée des systèmes installés dans le bâtiment doit permettre d’améliorer le confort des occupants tout en consommant moins d’énergie. Ces objectifs à optimiser sont pourtant antagonistes. Le problème résultant peut être alors vu comme un problème d’optimisation multicritères. Par ailleurs, pour répondre aux enjeux industriels, il devra être résolu non seulement dans une optique d’implémentation simple et peu coûteuse, avec notamment un nombre réduit de capteurs, mais aussi dans un souci de portabilité pour que le contrôleur résultant puisse être implanté dans des bâtiments d’orientation différente et situés dans des lieux géographiques variés.L’approche choisie est de type commande prédictive (MPC, Model Predictive Control) dont l’efficacité pour le contrôle du bâtiment a déjà été illustrée dans de nombreux travaux, elle requiert cependant des efforts de calcul trop important. Cette thèse propose une méthodologie pour la synthèse des contrôleurs, qui doivent apporter une performance satisfaisante en imitant les comportements du MPC, tout en répondant à des contraintes industriels. Elle est divisée deux grandes étapes :1. La première étape consiste à développer un contrôleur MPC. De nombreux défis doivent être relevés tels que la modélisation, le réglage des paramètres et la résolution du problème d’optimisation.2. La deuxième étape applique différents algorithmes d’apprentissage automatique (l’arbre de décision, AdaBoost et SVM) sur une base de données obtenue à partir de simulations utilisant le contrôleur prédictif développé. Les grands points levés sont la construction de la base de données, le choix de l’algorithme de l’apprentissage et le développement du contrôleur logique.La méthodologie est appliquée dans un premier temps à un cas simple pour piloter un volet,puis validée dans un cas plus complexe : le contrôle coordonné du volet, de l’ouvrant et dusystème de ventilation. / An efficient and coordinated control of systems in buildings should improve occupant comfort while consuming less energy. However, these objectives are antagonistic. It can then be formulated as a multi-criteria optimization problem. Moreover, it should be solved not only in a simple and cheap implementation perspective, but also for the sake of adaptability of the controller which can be installed in buildings with different orientations and different geographic locations.The MPC (Model Predictive Control) approach is shown well suited for building control in the state of the art but it requires a big computing effort. This thesis presents a methodology to develop logical controllers for equipments in buildings. It helps to get a satisfactory performance by mimicking the MPC behaviors while dealing with industrial constraints. Two keys steps are required :1. In the first step, an optimal controller is developed with hybrid MPC technique. There are challenges in modeling, parameters tuning and solving the optimization problem.2. In the second step, different Machine Learning algorithms (Decision tree, AdaBoost, SVM) are tested on database which is obtained with the simulation with the MPC controller. The main points are the construction of the database, the choice of learning algorithm and the development of logic controller.First, our methodology is tested on a simple case study to control a blind. Then, it is validatedwith a more complex case : development of a coordinated controller for a blind, natural ventilationand mechanical ventilation.

Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of the region defined by constraints. Necessary and sufficient conditions for these subset relations follow from duality theory, and it is possible to apply these conditions to constrain predicted system states and inputs with only a little conservatism. This leads to a general method of MPC design for uncertain-parameter systems. The resulting controllers have strong theoretical properties, can be implemented using standard algorithms and outperform existing techniques. Crucially, the online optimisation used in the controller is a convex problem with a number of constraints and variables that increases only linearly with the length of the prediction horizon. This holds true for both LDI and LPV systems. For the latter it is possible to optimise over a class of gain-scheduled control policies to improve performance, with a similar linear increase in problem size. The framework extends to stochastic LDIs with chance constraints, for which there are efficient suboptimal methods using online sampling. Sample approximations of chance constraint-admissible sets are generally not positively invariant, which motivates the novel concept of ‘sample-admissible' sets with this property to ensure recursive feasibility when using sampling methods. The thesis concludes by introducing a simple, convex alternative to chance-constrained MPC that applies a robust bound to the time average of constraint violations in closed-loop.

De nos jours, les éoliennes contribuent à une large partie de production d'énergie dans le monde. En 2013, 2,7% de la production d'électricité mondiale était éolienne, avec un objectif d'atteindre 14% de la demande d'électricité totale en 2020. Pour satisfaire ces exigences, la taille standard de la turbine éolienne tend à grandir. Les éoliennes de tailles des mégawatts sont très coûteuses, et leur rendement devrait être optimisé pour maximiser l'énergie produite et protéger les équipements de toute dégradation pour optimiser leur durée de vie.Dans ce projet de thèse, la commande prédictive à base de modèle (MPC) est utilisée pour la commande et la commande tolérante aux défauts de l'éolienne. Afin d'optimiser le temps de calcul de la commande MPC, qui peut rendre son implémentation en ligne irréalisable, les entrées de la commande ont été paramétrées par les fonctions de Laguerre (LMPC) ou les fonctions de Kautz (KMPC). Ceci a permis de réduire le temps de calcul d'un tiers. La commande MPC robuste par approche min-max a également été considérée dans l'objectif de rendre la stratégie de commande robuste aux incertitudes paramétriques, et à l'apparition de défauts. Ces différentes stratégies ont état évaluées sur un modèle de l'éolienne à deux masses, avec une commande multi entrée/multi avec contraintes sur les entrées et les sorties.Dans le chapitre V, la commande MPC paramétrée par les fonctions de Laguerre ou de Kautz a été reformulée dans l'unique objectif de compenser le défaut. En effet, sur une éolienne en fonctionnement stable et possédant des lois de commande qui ne s'accommode pas aux défauts, il est possible de calculer la correction nécessaire à considérer par les lois existantes afin de compenser le défaut, si le défaut est bien détecté et estimé. Cette stratégie est recherchée si l'industriel ne souhaite pas changer les lois de commande établies sur l'éolienne, car les stratégies de commande MPC discutées peuvent faire l'ensemble de travail : poursuite de la trajectoire désirée et l'accommodation aux défauts / Nowadays, wind turbines contribute to a large part of energy production in the world. In 2013, 2.7% of global electricity production was based on wind power, with a goal of reaching 14% of total electricity demand in 2020. The progression was remarkable in the last years, namely in France where the wind power generation increased from 2.5 TWh (terawatt-hour) in 2013 to 21.1 TWh in 2015.In order to satisfy these objectives, the standard size of the wind turbine tends to grow. However, the megawatt size wind turbines are very expensive and thus their efficiency has to be optimized in order to maximize the produced energy. Furthermore, it is aimed to protect the equipment from damage and maximize the service life of wind turbines, which is usually 20 years.In this thesis, model predictive control (MPC) is used to control the wind turbine and to identify the faults that could occur. Since the computation time in the MPC strategy is high, its use in real time fast systems may become unfeasable. To overcome this difficulty, the MPC control inputs are parametrized by Laguerre functions (LMPC) or Kautz functions (KMPC). This allowed decreasing the computation time by 33% compared to non-parametrized MPC. The min-max MPC approach is also considered in order to render the control strategy robust to parametric uncertainties and faults scenarios.These control strategies are evaluated on a wind turbine model with a multi-input (pitch angle and generator torque) / multi- output (generator power and generator speed) control, with constraints on inputs and outputs. These results are discussed in Chapter IV.In Chapter V, the Laguerre or Kautz parameterized MPC is reformulated with the objective of faults compensations. Indeed, if the faults are detected and estimated then it is possible to calculate the correction required to compensate these faults. This strategy becomes interesting from a wind turbine is operated with a controller that is not aimed to be changed for security or cost reasons, and the objective of the operator is only to compensate actuator or sensor faults. In these simulations, an available benchmark was used with the controller implemented in it.The thesis also contains a bibliographic and three introductory chapters discussing the state of the art of the turbine model, its control, fault detection and the MPC strategies used in this work

Une utilisation efficace et coordonnée des systèmes installés dans le bâtiment doit permettre d’améliorer le confort des occupants tout en consommant moins d’énergie. Ces objectifs à optimiser sont pourtant antagonistes. Le problème résultant peut être alors vu comme un problème d’optimisation multicritères. Par ailleurs, pour répondre aux enjeux industriels, il devra être résolu non seulement dans une optique d’implémentation simple et peu coûteuse, avec notamment un nombre réduit de capteurs, mais aussi dans un souci de portabilité pour que le contrôleur résultant puisse être implanté dans des bâtiments d’orientation différente et situés dans des lieux géographiques variés.L’approche choisie est de type commande prédictive (MPC, Model Predictive Control) dont l’efficacité pour le contrôle du bâtiment a déjà été illustrée dans de nombreux travaux, elle requiert cependant des efforts de calcul trop important. Cette thèse propose une méthodologie pour la synthèse des contrôleurs, qui doivent apporter une performance satisfaisante en imitant les comportements du MPC, tout en répondant à des contraintes industriels. Elle est divisée deux grandes étapes :1. La première étape consiste à développer un contrôleur MPC. De nombreux défis doivent être relevés tels que la modélisation, le réglage des paramètres et la résolution du problème d’optimisation.2. La deuxième étape applique différents algorithmes d’apprentissage automatique (l’arbre de décision, AdaBoost et SVM) sur une base de données obtenue à partir de simulations utilisant le contrôleur prédictif développé. Les grands points levés sont la construction de la base de données, le choix de l’algorithme de l’apprentissage et le développement du contrôleur logique.La méthodologie est appliquée dans un premier temps à un cas simple pour piloter un volet,puis validée dans un cas plus complexe : le contrôle coordonné du volet, de l’ouvrant et dusystème de ventilation. / An efficient and coordinated control of systems in buildings should improve occupant comfort while consuming less energy. However, these objectives are antagonistic. It can then be formulated as a multi-criteria optimization problem. Moreover, it should be solved not only in a simple and cheap implementation perspective, but also for the sake of adaptability of the controller which can be installed in buildings with different orientations and different geographic locations.The MPC (Model Predictive Control) approach is shown well suited for building control in the state of the art but it requires a big computing effort. This thesis presents a methodology to develop logical controllers for equipments in buildings. It helps to get a satisfactory performance by mimicking the MPC behaviors while dealing with industrial constraints. Two keys steps are required :1. In the first step, an optimal controller is developed with hybrid MPC technique. There are challenges in modeling, parameters tuning and solving the optimization problem.2. In the second step, different Machine Learning algorithms (Decision tree, AdaBoost, SVM) are tested on database which is obtained with the simulation with the MPC controller. The main points are the construction of the database, the choice of learning algorithm and the development of logic controller.First, our methodology is tested on a simple case study to control a blind. Then, it is validatedwith a more complex case : development of a coordinated controller for a blind, natural ventilationand mechanical ventilation.

This thesis explores the possibility of using an optimal control scheme called Model Predictive Control (MPC), to control climatization systems for electric vehicles. Some components of electric vehicles, for example the batteries and power electronics, are sensitive to temperature and for this reason it is important that their temperature is well regulated. Furthermore, like all vehicles, the cab also needs to be heated and cooled. One of the weaknesses of electric vehicles is their range, for this reason it is important that the temperature control is energy efficient. Once the range of electric vehicles is increased the down sides compared to traditional combustion engine vehicles decrease, which could lead to an increase in the usage of electric vehicles. This could in turn lead to a decrease of greenhouse gas emission in the transportation sector. With the help of MPC it is possible for the controller to take more factors into consideration when controlling the system than just temperature and in this thesis the power consumption and noise are also taken into consideration. A simple model where parts of the climate system’s circuits were seen as point masses was developed, with nonlinear heat transfers occurring between them, which in turn were controlled by actuators such as fans, pumps and valves. The model was created using Simulink and MATLAB, and the MPC toolbox was used to develop nonlinear MPC controllers to control the climate system. A standard nonlinear MPC, a nonlinear MPC with custom cost functions and a PI controller where all developed and compared in simulations of a cooling scenario. The controllers were designed to control the temperatures of the battery, power electronics and the cab of an electric vehicle. The results of the thesis indicate that MPC could reduce power consumption for the climate control system, it was however not possible to draw any final conclusions as the PI controller that the MPC controllers were compared to was not well optimized for the system. The MPC controllers could benefit from further work, most importantly by applying a more sophisticated tuning method to the controller weights. What was certain was that it is possible to apply this type of centralized controller to very complex systems and achieve robustness without external logic. Even with the controller keeping track of six different temperatures and controlling 15 actuators, the control loop runs much faster than real time on a modern computer which shows promise with regard to implementing it on an embedded system.

This thesis compares Model Predictive Control (MPC) and Proportional-Integral (PI) control in a pin-on-disc tribometer. The tribometer consists of a geared Direct Current (DC) motor driving a flywheel and a voice coil actuating a pin onto the surface of the flywheel. The contact surface of both pin and disc can be replaced with the desired material. The system is first modeled, and both conntrollers are derived from the models and Matlab and Simulink. A PI-cascade controller is used for the disc while a single PI controller is used for the pin. The PI controller is derived using the pole placement, the MPC is derived using the Simulink MPC-designer.  The control methods are evaluated using the Root Mean Sqaure Error (RMSE), percentage overshoot and rise time of step responses from two cases: The ideal case and the operational case. The ideal case contians minimal disturbance as only one actuator is operating at a time. The operational case intends to depict the future use case of the test rig where both actuators are operating simultaneously.  Statistical analysis is conducted as a three was Analysis of Variance (ANOVA) between controller (MPC/PI), case (ideal/operational) and actuator level (40/80 N or 100/200 rpm). In conclusion the MPC has a faster rise time but a higher RMSE than the PI controller in both the ideal and operational case when controlling the disc application of a tribometer. No significant difference in overshoot is established in the disc application of the tribometer. Considering the pin system of the tribometer, no conclusion can be drawn. The main factor being the large impact of the noise at the sampling frequency. This causes failure of the controllers in the operational case. / Denna avhandling jämför Model Predictive Control (MPC) och Propertionell-Integral (PI) kontroll i en stift-på-skiva tribometer. Tribometern består av en likströmsmotor med en växellåda som driver ett svänghjul, och en talspole som är kopplat till ett stift som i sin tur applicerar kraft på svänghjulets yta. Kontaktytan för både stift och skiva kan ersättas med önskat material. Systemet modelleras först, och de båda reglersystemen härleds från modellerna i Matlab och Simulink. En PI-kaskad kontroller används för skivan medan en enkel PI-kontroller används för stiftet. PI-kontrollern härleds med hjälp av polplacering MPC-kontrollern härleds med hjälp av MPC-designern i Simulink.  Reglermetoderna utvärderas med hjälp av kvadratroten ur medelkvadratavvikelsen (RMSE), procentuell översläng och stigtid för stegsvar från två fall: Det ideala fallet och de operativa fallet. Det ideala fallet innehåller minimala störningar eftersom endast ett ställdon åt gången är i drift. Det operativa fallet är avsett att representera det framtida användningsområdet för testriggen där båda ställdonen är i drift samtidigt.  Den statistiska analysen utförs som en trevägs Anlysis of Variance (ANOVA) mellan kontroll (MPC/PI), fall (idealt/operativt) och nivå (40/80 N eller 100/200 rpm). Slutsatsen är att MPC-kontrollern har en snabbare stigtid med en högre RMSE än PI-kontrollern i både idealfallet och driftfallet vid styrning av skivan i en tribometer. Ingen signifikant skillnad i översläng konstaterades i skivan. Ingen slutsats kan dras när det gäller drivning av tribometerns stift. Den huvudsakliga faktorn är den stora mängden störningar vid samplingsfrekvensen. Detta leder till att kontrollerna misslyckas in det operativa fallet.