Research in soft robot hardware has led to the development of platforms that allow for safer performance when working in uncertain or dynamic environments. The potential of these platforms is limited by the lack of proper dynamic models to describe or controllers to operate them. A common difficulty associated with these soft robots is a representation for torque, the common electromechanical relation seen in motors does not apply. In this thesis, several different torque models are presented and used to construct linear state-space models. The control limitations on soft robots are induced by natural compliance inherent to the hardware. This inherent compliance results in soft robots that are commonly underdamped and present significant oscillations when accelerated quickly. These oscillations can be mitigated through model-based controllers which can anticipate these oscillations. In this thesis, multiple model predictive controllers are implemented with the torque models produced and results are presented for an inflatable single-DoF pneumatically actuated soft robot. Larger, multi-DoF, soft robots present additional issues with control, where flexibility in one joint impacts control in others. In this thesis a preliminary method and results for controlling multiple joints on an inflatable multi-DoF pneumatically actuated soft robot are presented. While model predictive controllers are capable, their control commands are defined by solving an optimization constrained by model dynamics. This optimization relies on minimizing the cost of a user-defined objective function. This objective function contains a series of weights, which allow the user to tune the importance of each component in the objective function. As there are no calculations that can be performed to tune model predictive controllers to achieve superior control performance, they often need to be tuned tediously by a skilled operator. In this thesis, a method for automated discrete performance identification and model predictive controller weight tuning is presented. This thesis constructs multiple state-space models for single- and multi-DoF underdamped, antagonistic, pneumatically actuated soft robots and shows that these models can be used with model predictive control, tuned for performance, to achieve accurate joint position control.

Syftet med mitt arbete var att jag ville ta reda på hur jag kunde effektivisera min skapandeprocess för att kunna färdigproducera en hiphoplåt om dagen.  Jag producerade fyra låtar. Genom att kartlägga mina beteenden i min skapandeprocess kunde jag med hjälp av litteraturstudier plocka fram metoder för att öka min kreativitet och effektivitet.

Cette thèse propose une méthodologie hors ligne pour la robustification de lois de commande prédictives multivariables, se basant sur une problématique d'optimisation convexe d'un paramètre de Youla. Le point de départ de la démarche consiste à synthétiser une loi de commande initiale prédictive multivariable sous forme d'état qui stabilise le système. Le but est de garantir la robustesse en stabilité face à des incertitudes non structurées et d'assurer des performances nominales pour le rejet de perturbations, imposées sous la forme des gabarits temporels sur les sorties. Ce problème d'optimisation est résolu par un formalisme LMI. Le paramètre de Youla obtenu permet de gérer d'une part le compromis entre la robustesse en stabilité et les performances nominales et d'une autre part permet de réduire l'influence du couplage multivariable sur le rejet des perturbations.<br />Le cas de systèmes incertains appartenant à un ensemble donné d'incertitudes polytopiques est également traité. Deux possibilités sont analysées : le correcteur MPC initial stable sur tout le domaine polytopique, le correcteur MPC initial instable sur une partie du domaine incertain considéré. Dans les deux cas, une condition supplémentaire BMI est ajoutée pour chaque sommet du polytope considéré. Il s'agit de deux problèmes d'optimisation non-convexe pour lesquels deux solutions de complexité raisonnable sous une forme LMI sous-optimale sont proposées.<br />Cette technique de robustification est illustrée sur un modèle académique multivariable d'un réacteur. Une application à un robot médical est ensuite détaillée. L'ensemble des stratégies développées pour réduire l'influence des incertitudes non structurées sur le système en respectant les gabarits imposés sur les sorties pour le rejet de perturbations a donné lieu à la mise au point d'un logiciel sous MATLABTM.

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.

<p>This thesis studies the negative impact that control signal saturation may have on a controlled system. Different methods that are used to compensate for this problem are also studied and evaluated. Both sensitivity to disturbances and the effect the method has on the systems'ability to follow a reference signal will be examined. Stability will be discussed, but no conclusions whether the systems are stabilized or not can be drawn. </p><p>Control signal saturation will lead to a slower behavior in general. For controllers with integral action this performance degradation will cause an extended growth in the integrating part of the controller. This leads to large overshoots and possibly to instability. </p><p>As an alternative to the more ad-hoc based methods, model based predictive control is studied. This metod can explicitly handle constrained control signals. Here, too, sensitivity to disturbances and the effect the method has on the systems'ability to follow a reference signal is examined.</p>

<p>The situation when driving in dense traffic and at low speeds is called Stop and Go. A controller for automatic following of the car in front could under these conditions reduce the driver's workload and keep a safety distance to the preceding vehicle through different choices of gear and engine torque. The aim of this thesis is to develop such a controller, with an additional focus on lowering the fuel consumption. With help of GPS, 3D-maps and sensors information about the slope of the road and the preceding vehicle can be obtained. Using this information the controller is able to predict future possible control actions and an optimization algorithm can then find the best inputs with respect to some criteria. The control method used is Model Predictive Control (MPC) and as the name indicate a model of the control object is required for the prediction. To find the optimal sequence of inputs, the optimization method Dynamic Programming choose the one which lead to the lowest fuel consumption and satisfactory following. Simulations have been made using a reference trajectory which was measured in a real traffic jam. The simulations show that it is possible to follow the preceding vehicle in a good way and at the same time reduce the fuel consumption with approximately 3 %.</p>

<p>The purpose with this masters thesis was to model the scanner in a system for laser bathymetry. The model was then used to develop a controller for the scanner so a good search pattern was achieved. </p><p>Two different types of models have been tested, a physical model and a Black Box model of Box Jenkins type. The physical model has been derived from Lagranges equations. Identification experiments have been used to compute the Black Box model and to find the unknown parameters in the physical model. </p><p>Three different controllers have been tested, a PID controller, a model predictive controller and a controller with feedforward. The controller with feedforward gave the best result. By softening the reference signal and using feedforward a good search pattern was achieved.</p>

<p>The department REI/EP at DaimlerChrysler Research and Technology and the Laboratory for Efficient Energy Systems at Trier University of Applied Science, are developing control functions and fuel optimal strategies for low speed conditions. The goal of this thesis project was to further develop the fuel optimal operating strategies, and implement them into a test vehicle equipped with a dSPACE environment. This was accomplished by making optimal reference signals using dynamic programming. Optimal, in this case, means signals that results in low fuel consumption, comfortable driving, and a proper distance to the preceding vehicle. These reference signals for the velocity and distance are used by an MPC controller (Model Predictive Control) to control the car. In every situation a suitable reference path is chosen, depending on the velocities of both vehicles, and the distance. The controller was able to follow another vehicle in a proper way. The distance was kept, the driving was pleasant, and it also seems like it is possible to save fuel. When accepting some deviations in distance to the preceding car, a fuel reduction of 8 % compared to the car in front can be achieved.</p>

<p>Antibiotic-resistant bacteria are increasingly a major healthcare problem but very few new classes of antibiotics have been discovered or launched in recent decades. Approaches to dealing with the problem include learning how bacteria evolve to resistance and improving dosing regimens with current antibiotics so as to reduce the selection of resistant bacteria. </p><p>This thesis presents studies examining whether antibiotic dosing at high levels can prevent the selection of fluoroquinolone-resistant mutants in <i>Escherichia coli</i>. It also addresses the genetics of fluoroquinolone resistance in <i>E. coli</i> in relation to fitness costs for the resistant bacteria, and the evolution of <i>E. coli</i> to reduce the costs of resistance.</p><p>The mutant prevention concentration (MPC) of ciprofloxacin was measured for a set of clinical urinary tract infection <i>E. coli</i> strains showing that MPC could not be predicted from the minimum inhibitory concentration (MIC). Results from an <i>in vitro</i> kinetic model showed that an AUC/MPC >22 for ciprofloxacin was the single best pharmacodynamic index that predicted prevention of resistance emergence in the wild-type. Simulating currently approved dosing regimens for three different fluoroquinolones it was found that only a few were effective in preventing the selection of a small sub-population of pre-existing mutants. </p><p>Step-wise selection of fluoroquinolone resistance showed that the accumulation of mutations usually reduced bacterial fitness<i> in vitro</i> and <i>in vivo</i>. Systematic construction of isogenic resistant strains confirmed this result and revealed that some combinations of resistance mutations mutually compensate and increase both resistance and fitness. It was discovered that mutations altering RNA polymerase could ameliorate the fitness costs of fluoroquinolone resistance. Thus, the major fitness cost of fluoroquinolone resistance is due to defective transcription. </p><p>The finding that fluoroquinolone resistance mutations can increase resistance while mutually compensating their fitness costs, shows that resistance to fluoroquinolones can continue to evolve in the absence of antibiotic selection.</p>

<p>The emergence of multidrug resistant bacteria world wide is a serious problem, and very few new drugs are under development. The selection of resistant bacteria is affected by factors such as mutation rate, biological fitness cost and the rate of fitness compensation. This thesis is focused on how mutation rate affects resistance to fluoroquinolones and on exploring a dosing strategy that might slow resistance development. </p><p>In a set of urinary tract <i>Escherichia coli</i> isolates MIC values above the breakpoint for the fluoroquinolones norfloxacin and ciprofloxacin carried at least three resistance-associated mutations. In these isolates the number of resistance mutations correlated with the mutation rate. During step-wise selection for decreased susceptibility to fluoroquinolones, the accumulation of mutations in <i>E. coli</i> was associated with an increasing biological cost both <i>in vitro</i> and <i>in vivo</i>. However, in some lineages an additional selection step for resistance was associated with a partial restoration of fitness. During step-wise selections we found, as expected, that reduced ciprofloxacin susceptibility frequently hitchhiked with a strong mutator phenotype. More surprisingly, we also found that reduced susceptibility was frequently associated with the emergence of rifampicin-resistant populations. We hypothesise that this correlation reflects selection for fitness-compensating mutations in RNA polymerase.</p><p>Mutant prevention concentration (MPC) dosing has been proposed as a strategy to reduce the selection of resistant bacterial populations. Based on limited data it had been thought that MPC might be a simple multiple of MIC, which can easily be determined. However, we showed for a collection of susceptible urinary tract <i>E. coli </i>that MPC could not be predicted from MIC and must be measured directly for relevant populations. Using an <i>in vitro</i> kinetic model we also showed that the pharmacodynamic index that best predicted prevention of resistance development in wild type <i>E. coli</i> was an AUC/MPC of > 22 for ciprofloxacin.</p>