The growth of the automobile Industry in the past 50 years is radical. The development of chassis control systems have grown drastically due to the demand for safer, faster and more comfortable vehicles. For example, the invention of Anti-lock Braking System (ABS) has resulted in saving more than a million lives since its adaptation while also allowing the vehicles to commute faster. As we move into the autonomous vehicles era, demand for additional information about tire-road interaction to improve the performance of the onboard chassis control systems, is high. This is due to the fact that the interaction between the tire and the road surface determines the stability boundary limits of the vehicles. In this research, a real-time system to classify the road surface into five major categories was developed. The five surfaces include Dry Asphalt, Wet Asphalt, Snow, and Ice and dry Concrete. tri-axial accelerometers were placed on the inner liner of the tires. An advanced signal processing technique was utilized along with a machine learning model to classify the road surfaces. The instrumented Volkswagen Jetta with intelligent tires was retrofitted with new instrumentation for collecting data and evaluating the performance of the developed real-time system. A comprehensive study on road surface classification was performed in order to determine the features of the classification algorithm. Performance of the real-time system is discussed in details and compared with offline results. / Master of Science / The automobile industry has been improving road transportation safety over the past 50 years. While we enter the autonomous vehicles era, the safety of the vehicle is of primary concern. In order to get the autonomous vehicles to production, we will have to improve the on board vehicle control systems to adapt to all surfaces. Gaining more accurate information about the tire and road interaction will help in improving the control systems. Tires have always been considered a passive element of the vehicle. However, more recently, the idea of “tire as a sensor” has surfaced and has become one of the major research thrusts in tire as well as vehicle companies. The intelligent tire research at the Center for Tire Research (CenTiRe) begun in 2010 and has been going strong. In this work, we have developed a classification algorithm to classify the road surfaces in real-time based on acceleration measured inside the tire. The information regarding the road surface would be highly beneficial for the developing new control strategies, automate service vehicles and aid surface prediction in autonomous vehicles.

In this thesis, the primary powertrain components of a power split hybrid electric vehicle are modeled. In particular, the dynamic model of the energy storage element (i.e., traction battery) is exactly linearized through an input transformation method to take advantage of the proposed optimal control algorithm. A lipschitz continuous and nondecreasing cost function is formulated in order to minimize the net amount of consumed fuel. The globally optimal solution is obtained using a dynamic programming routine that produces the optimal input based on the current state of charge and the future power demand. It is shown that the global optimal control solution can be expressed in closed form for a time invariant and convex incremental cost function utilizing the interval back propagation approach. The global optimality of both time varying and invariant solutions are rigorously proved. The optimal closed form solution is further shown to be applicable to the time varying case provided that the time variations of the incremental cost function are sufficiently small. The real time implementation of this algorithm in Simulink is discussed and a 32.84 % improvement in fuel economy is observed compared to existing rule based methods.

There is a demand for renewable energy in today’s society. Wave energy is a nearly untapped source of renewable energy. Ocean Harvesting Technologies AB (OHT) is currently developing a device that can be used to convert wave energy into electricity. The device is a Wave Energy Converter of the type point absorber. Their concept is a floating buoy that is connected to the seafloor via a Power Take-Off (PTO) unit. The PTO unit is equipped with generators, which are used to convert kinetic energy of the buoy into electricity. The objective of this thesis is to control the generators to optimize the performance of the system. OHT was interested in knowing how their system performs under the influence of a controller based on MPC. Hence an MPC-controller is constructed in this thesis. The developed controller functions by predicting the states (position and velocity) of the buoy over a finite time (e.g. $5s$). Then the controller uses the predictions to find a control force that makes the system behave optimally for the next $5$ seconds. A requirement from the company is that the controller should find the control force based on how the buoy is predicted to move in 3 Degrees Of Freedom (DOF). Further, the controller should be able to operate in real-time. To meet the company’s requirements, the following is done. A linear 3ODF model of the system is derived. This is used to predict the states of the buoy in the controller. An MPC algorithm is constructed. In this, the linear model and constraints of the system are included. Then, a simulation environment is built. This is including a non-linear model of OHT’s system. The performance of the controller is tested in the simulation environment. Real-time implementation is an important aspect of the controller. The computational time required by the controller is measured in the simulations. The results imply that the controller stands a chance of being real-time implementable. However, make sure that it can be run in real-time it should be tested on the control unit that OHT plans to use in their system. A linear model of the system is used in the controller to predict the future states o the buoy. It is important that the predictions are accurate for the controller to control the system in an optimal way. Hence, the validity of the linear model is investigated. The controller is managing to predict some states better than others. However, the controller is doing a fine job with controlling the system in terms of generated power. Thus the linear model is considered to be valid for the application. An advantage with controllers based on MPC is the simplicity of tuning the controller. Changes of settings in the controller have a predictable effect on the results. For the settings found in this thesis, the system is performing fine in terms of power generation. However, more work is needed to find more optimal settings.

The work concerns the "observability problem"--the reconstruction of a dynamic process's full state from a partially measured state-- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc. . . We propose a solution to these problems, the Adaptive High-Gain (EKF). Observability theory reveals the existence of special representations characterizing nonlinear systems having the observability property. Such representations are called observability normal forms. A EKF variant based on the usage of a single scalar parameter, combined with an observability normal form, leads to an observer, the High-Gain EKF, with improved performance when the estimated state is far from the actual state. Its convergence for any initial estimated state is proven. Unfortunately, and contrary to the EKF, this latter observer is very sensitive to measurement noise. Our observer combines the behaviors of the EKF and of the high-gain EKF. Our aim is to take advantage of both efficiency with respect to noise smoothing and reactivity to large estimation errors. In order to achieve this, the parameter that is the heart of the high-gain technique is made adaptive. Voila, the Adaptive High-Gain EKF. A measure of the quality of the estimation is needed in order to drive the adaptation. We propose such an index and prove the relevance of its usage. We provide a proof of convergence for the resulting observer, and the final algorithm is demonstrated via both simulations and a real-time implementation. Finally, extensions to multiple output and to continuous-discrete systems are given.

[EN] Common audio systems are designed with the intent of creating real and immersive scenarios that allow the user to experience a particular acoustic sensation that does not depend on the room he is perceiving the sound. However, acoustic devices and multichannel rendering systems working inside a room, can impair the global audio effect and thus the 3D spatial sound. In order to preserve the spatial sound characteristics of multichannel rendering techniques, adaptive filtering schemes are presented in this dissertation to compensate these electroacoustic effects and to achieve the immersive sensation of the desired acoustic system. Adaptive filtering offers a solution to the room equalization problem that is doubly interesting. First of all, it iteratively solves the room inversion problem, which can become computationally complex to obtain when direct methods are used. Secondly, the use of adaptive filters allows to follow the time-varying room conditions. In this regard, adaptive equalization (AE) filters try to cancel the echoes due to the room effects. In this work, we consider this problem and propose effective and robust linear schemes to solve this equalization problem by using adaptive filters. To do this, different adaptive filtering schemes are introduced in the AE context. These filtering schemes are based on three strategies previously introduced in the literature: the convex combination of filters, the biasing of the filter weights and the block-based filtering. More specifically, and motivated by the sparse nature of the acoustic impulse response and its corresponding optimal inverse filter, we introduce different adaptive equalization algorithms. In addition, since audio immersive systems usually require the use of multiple transducers, the multichannel adaptive equalization problem should be also taken into account when new single-channel approaches are presented, in the sense that they can be straightforwardly extended to the multichannel case. On the other hand, when dealing with audio devices, consideration must be given to the nonlinearities of the system in order to properly equalize the electroacoustic system. For that purpose, we propose a novel nonlinear filtered-x approach to compensate both room reverberation and nonlinear distortion with memory caused by the amplifier and loudspeaker devices. Finally, it is important to validate the algorithms proposed in a real-time implementation. Thus, some initial research results demonstrate that an adaptive equalizer can be used to compensate room distortions. / [ES] Los sistemas de audio actuales están diseñados con la idea de crear escenarios reales e inmersivos que permitan al usuario experimentar determinadas sensaciones acústicas que no dependan de la sala o situación donde se esté percibiendo el sonido. Sin embargo, los dispositivos acústicos y los sistemas multicanal funcionando dentro de salas, pueden perjudicar el efecto global sonoro y de esta forma, el sonido espacial 3D. Para poder preservar las características espaciales sonoras de los sistemas de reproducción multicanal, en esta tesis se presentan los esquemas de filtrado adaptativo para compensar dichos efectos electroacústicos y conseguir la sensación inmersiva del sistema sonoro deseado. El filtrado adaptativo ofrece una solución al problema de salas que es interesante por dos motivos. Por un lado, resuelve de forma iterativa el problema de inversión de salas, que puede llegar a ser computacionalmente costoso para los métodos de inversión directos existentes. Por otro lado, el uso de filtros adaptativos permite seguir las variaciones cambiantes de los efectos de la sala de escucha. A este respecto, los filtros de ecualización adaptativa (AE) intentan cancelar los ecos introducidos por la sala de escucha. En esta tesis se considera este problema y se proponen esquemas lineales efectivos y robustos para resolver el problema de ecualización mediante filtros adaptativos. Para conseguirlo, se introducen diferentes esquemas de filtrado adaptativo para AE. Estos esquemas de filtrado se basan en tres estrategias ya usadas en la literatura: la combinación convexa de filtros, el sesgado de los coeficientes del filtro y el filtrado basado en bloques. Más especificamente y motivado por la naturaleza dispersiva de las respuestas al impulso acústicas y de sus correspondientes filtros inversos óptimos, se presentan diversos algoritmos adaptativos de ecualización específicos. Además, ya que los sistemas de audio inmersivos requieren usar normalmente múltiples trasductores, se debe considerar también el problema de ecualización multicanal adaptativa cuando se diseñan nuevas estrategias de filtrado adaptativo para sistemas monocanal, ya que éstas deben ser fácilmente extrapolables al caso multicanal. Por otro lado, cuando se utilizan dispositivos acústicos, se debe considerar la existencia de no linearidades en el sistema elactroacústico, para poder ecualizarlo correctamente. Por este motivo, se propone un nuevo modelo no lineal de filtrado-x que compense a la vez la reverberación introducida por la sala y la distorsión no lineal con memoria provocada por el amplificador y el altavoz. Por último, es importante validar los algoritmos propuestos mediante implementaciones en tiempo real, para asegurarnos que pueden realizarse. Para ello, se presentan algunos resultados experimentales iniciales que muestran la idoneidad de la ecualización adaptativa en problemas de compensación de salas. / [CA] Els sistemes d'àudio actuals es dissenyen amb l'objectiu de crear ambients reals i immersius que permeten a l'usuari experimentar una sensació acústica particular que no depèn de la sala on està percebent el so. No obstant això, els dispositius acústics i els sistemes de renderització multicanal treballant dins d'una sala poden arribar a modificar l'efecte global de l'àudio i per tant, l'efecte 3D del so a l'espai. Amb l'objectiu de conservar les característiques espacials del so obtingut amb tècniques de renderització multicanal, aquesta tesi doctoral presenta esquemes de filtrat adaptatiu per a compensar aquests efectes electroacústics i aconseguir una sensació immersiva del sistema acústic desitjat. El filtrat adaptatiu presenta una solució al problema d'equalització de sales que es interessant baix dos punts de vista. Per una banda, el filtrat adaptatiu resol de forma iterativa el problema inversió de sales, que pot arribar a ser molt complexe computacionalment quan s'utilitzen mètodes directes. Per altra banda, l'ús de filtres adaptatius permet fer un seguiment de les condicions canviants de la sala amb el temps. Més concretament, els filtres d'equalització adaptatius (EA) intenten cancel·lar els ecos produïts per la sala. A aquesta tesi, considerem aquest problema i proposem esquemes lineals efectius i robustos per a resoldre aquest problema d'equalització mitjançant filtres adaptatius. Per aconseguir-ho, diferent esquemes de filtrat adaptatiu es presenten dins del context del problema d'EA. Aquests esquemes de filtrat es basen en tres estratègies ja presentades a l'estat de l'art: la combinació convexa de filtres, el sesgat dels pesos del filtre i el filtrat basat en blocs. Més concretament, i motivat per la naturalesa dispersa de la resposta a l'impuls acústica i el corresponent filtre òptim invers, presentem diferents algorismes d'equalització adaptativa. A més a més, com que els sistemes d'àudio immersiu normalment requereixen l'ús de múltiples transductors, cal considerar també el problema d'equalització adaptativa multicanal quan es presenten noves solucions de canal simple, ja que aquestes s'han de poder estendre fàcilment al cas multicanal. Un altre aspecte a considerar quan es treballa amb dispositius d'àudio és el de les no linealitats del sistema a l'hora d'equalitzar correctament el sistema electroacústic. Amb aquest objectiu, a aquesta tesi es proposa una nova tècnica basada en filtrat-x no lineal, per a compensar tant la reverberació de la sala com la distorsió no lineal amb memòria introduïda per l'amplificador i els altaveus. Per últim, és important validar la implementació en temps real dels algorismes proposats. Amb aquest objectiu, alguns resultats inicials demostren la idoneïtat de l'equalització adaptativa en problemes de compensació de sales. / Fuster Criado, L. (2015). Linear and nonlinear room compensation of audio rendering systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59459

Les réseaux radio actuelles utilisent le spectre inefficacement, car une bande de fréquence est allouée de façon permanente à une technologie spécifique. Vu que le spectre est une ressource limitée, cette attribution statique ne pourra bientôt plus combler les besoins des systèmes de transmission qui ne cessent de croître. On peut toutefois optimiser l'utilisation du spectre en permettant des transmissions secondaires (SU) dans les espaces libres du primaire (PU). Cette vision constitue l'objectif principal de la radio cognitive. Nous proposons d'évaluer les stratégies de transmission pour la coexistence des systèmes primaires (PU) et SU dans les mêmes réseaux. Plus concrètement, nous nous focalisons sur un scénario spatial interweave en émettant les signaux SU dans les espaces vides du PU à l'aide d'un précodeur linéaire. Néanmoins, ce précodage nécessite une connaissance a priori des canaux interférents. L'échange d'informations entre le PU et le SU étant proscrit, nous exploitons l'hypothèse de la réciprocité du canal. Cette hypothèse compense l'absence de coopération, mais elle n'est pas si évidente à exploiter en pratique à cause des perturbations des circuits radio fréquence. Nous suggérons de compenser ces perturbations par des méthodes de calibration relative. Nous proposons ensuite une implémentation temps-réel des solutions sur une plateforme LTE. Pour finir, nous généralisons l'approche RC à un système de transmission multi-utilisateurs, à travers une combinaison des techniques RC et massive MIMO, cette approche constitue s’établit comme une solution à la progression exponentielle du trafic. / The accelerated evolution of wireless transmission in recent years has dramatically increased the spectrum overcrowding. Indeed, the spectrum is inefficiently used in the conventional networks, since a frequency band is statically allocated to a specific technology called primary (PU). Whereas the radio spectrum is limited, this static frequency allocation will no longer be able to meet the increasing needs of bandwidth. However, the spectrum can be optimally used in enabling secondary (SU) transmissions, provided the latters do not harm the PU. This opportunistic vision of wireless transmissions is the main aim of Cognitive Radio (CR). CR enables smart use of wireless resources and is a key ingredient to perform high spectral efficiency. We focus on a spatial interweave (SIW) CR scenario which exploits the spatial white spaces to enable SU transmissions. The latter forms spatial beams using precoders, so that there is no interference towards the primary. Nevertheless, this precoding requires acquisition of the crosslink channel. However, due to the lack of cooperation between PU and SU, we acquire the channel thanks to channel reciprocity. Furthermore, the practical use of the reciprocity is not as straightforward as in theory, because it is is jeopardized by the nonreciprocal radio frequency front-ends. These perturbations are compensated in our study by relative calibration algorithms. Subsequently, we propose an implementation of our solutions in a real-time LTE platform. Eventually, we extend the CR model to a MU system in suggesting a combination of SIW and massive MIMO techniques. This scheme is an interesting candidate to overcome the exponential traffic growth.

Le travail porte sur la problématique de l’observation des systèmes — la reconstruction de l’état complet d’un système dynamique à partir d'une mesure partielle de cet état. Nous considérons spécifiquement les systèmes non linéaires. Le filtre de Kalman étendu (EKF) est l’un des observateurs les plus utilisés à cette fin. Il souffre cependant d’une performance moindre lorsque l'état estimé n’est pas dans un voisinage de l'état réel. La convergence de l’observateur dans ce cas n’est pas prouvée. Nous proposons une solution à ce problème : l’EKF à grand gain adaptatif. La théorie de l’observabilité fait apparaître l’existence de représentations caractérisant les systèmes dit observables. C’est la forme normale d’observabilité. L’EKF à grand gain est une variante de l’EKF que l’on construit à base d’un paramètre scalaire. La convergence de cet observateur pour un système sous sa forme normale d’observabilité est démontrée pour toute erreur d’estimation initiale. Cependant, contrairement à l’EKF, cet algorithme est très sensible au bruit de mesure. Notre objectif est de combiner l’efficacit´e de l’EKF en termes de lissage du bruit, et la r´eactivit´e de l’EKF grand-gain face aux erreurs d’estimation. Afin de parvenir à ce résultat nous rendons adaptatif le paramètre central de la méthode grand gain. Ainsi est constitué l’EKF à grand gain adaptatif. Le processus d’adaptation doit être guidé par une mesure de la qualité de l’estimation. Nous proposons un tel indice et prouvons sa pertinence. Nous établissons une preuve de la convergence de notre observateur, puis nous l’illustrons à l’aide d’une série de simulations ainsi qu’une implémentation en temps réel dur. Enfin nous proposons des extensions au résultat initial : dans le cas de systèmes multi-sorties et dans le cas continu-discret. / The work concerns the “observability problem”—the reconstruction of a dynamic process’s full state from a partially measured state— for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc. . . We propose a solution to these problems, the Adaptive High-Gain (EKF). Observability theory reveals the existence of special representations characterizing nonlinear systems having the observability property. Such representations are called observability normal forms. A EKF variant based on the usage of a single scalar parameter, combined with an observability normal form, leads to an observer, the High-Gain EKF, with improved performance when the estimated state is far from the actual state. Its convergence for any initial estimated state is proven. Unfortunately, and contrary to the EKF, this latter observer is very sensitive to measurement noise. Our observer combines the behaviors of the EKF and of the high-gain EKF. Our aim is to take advantage of both efficiency with respect to noise smoothing and reactivity to large estimation errors. In order to achieve this, the parameter that is the heart of the high-gain technique is made adaptive. Voila, the Adaptive High-Gain EKF. A measure of the quality of the estimation is needed in order to drive the adaptation. We propose such an index and prove the relevance of its usage. We provide a proof of convergence for the resulting observer, and the final algorithm is demonstrated via both simulations and a real-time implementation. Finally, extensions to multiple output and to continuous-discrete systems are given.