Soft robots and particularly inflatable robots are of interest because they are lightweight, compact, robust to impact, and can interact with humans and their environment relatively safely compared to rigid and heavy traditional robots. Improved safety is due to their low mass that results in low-energy collisions and their compliant, soft construction. Inflatable robots (which are a type of soft robot) are also robust to impact and have a high torque to weight ratio. As a result inflatable robots may be used for many applications such as space exploration, search and rescue, and human-robot interaction. One of the potential problems with inflatable or pneumatically actuated robots is air leaking from the structural or actuation chambers. In this thesis methods are demonstrated to detect leaks in the structural and actuation chambers of inflatable and pneumatically actuated robots. It is then demonstrated that leaks can be slowed by lowering a target pressure which affects joint stiffness to prolong the life of the system. To demonstrate the effects of lowering the target pressure it is first shown that there exists a trade-off between the commanded target pressures at steady-state and the steady-state error at the robot end effector under normal operation. It is then shown that lowering the target pressure (which is related to stiffness) can extend the operational life of the system when compressed air is a limited resource. For actuator leaks a lower target pressure for the leaking joint is used to demonstrate the trade-off between slowing the leak rate and system performance. For structural leaks a novel control algorithm is demonstrated to lower target pressure as much as possible to slow the leak while maintaining a user specified level of accuracy. The method developed for structural leaks extends the operational life of the robot. Long-term error during operation is decreased by as much as 50% of the steady-state error at the end effector when compared to performance during a leak without the control algorithm. For actuation leaks in a joint with a high-torque load the possibility of a 30% increase in operation time while only increasing steady-state error by 2 cm on average is demonstrated. For a joint with a low-torque load it is shown that up to a 300% increase in operation time with less than 1 cm increased steady-state error is possible. The work presented in this thesis demonstrates that varying stiffness may be used to extend the operational life of a robot when a leak has occurred. The work discussed here could be used to extend the available operation time of pneumatic robots. The methods and principles presented here could also be adapted for use on other types of robots to preserve limited system resources (e.g., electrical power) and extend their operation time.

This work enables accelerated fluid recovery in oil and gas reservoirs by automatically controlling fluid height and bottomhole pressure in wells. Several literature studies show significant increase in recovered oil by determining a target bottomhole pressure but rarely consider how to control to that value. This work enables those benefits by maintaining bottomhole pressure or fluid height. Moving Horizon Estimation (MHE) determines uncertain well parameters using only common surface measurements. A Model Predictive Controller (MPC) adjusts the stroking speed of a sucker rod pump to maintain fluid height. Pump boundary conditions are simulated with Mathematical Programs with Complementarity Constraints (MPCCs) and a nonlinear programming solver finds a solution in near real-time. A combined rod string, well, and reservoir model simulate dynamic well conditions, and are formulated for simultaneous optimization by large-scale solvers. MPC increases cumulative oil production vs. conventional pump off control by maintaining an optimal fluid level height.

Robots have been a revolutionizing force in manufacturing in the 20th and 21st century but have proven too dangerous around humans to be used in many other fields including medicine. We describe a new control algorithm for robots developed by the Brigham Young University Robotics and Dynamics and Robotics Laboratory that has shown potential to make robots less dangerous to humans and suitable to work in more applications. We analyze the computational complexity of this algorithm and find that it could be a feasible control for even the most complicated robots. We also show conditions for a system which guarantee local stability for this control algorithm.

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>