Robust Execution for Stochastic Hybrid Systems: Algorithms for Control, Estimation and Learning

Unmanned systems, such as Autonomous Underwater Vehicles (AUVs), planetary rovers and space probes, have enormous potential in areas such as reconnaissance and space exploration. However the effectiveness and robustness of these systems is currently restricted by a lack of autonomy. A model-based executive, which increases the level of autonomy can be used to simplify the operator¿s task and leave degrees of freedom in the plan that allow the executive to optimize resources and ensure robustness to uncertainty. Uncertainty arises due to uncertain state estimation, disturbances, model uncertainty and component failures. This book develops a model-based executive that reasons explicitly from a stochastic hybrid discrete-continuous system model to find the optimal course of action, while ensuring the required level of robustness to uncertainty is achieved. The executive makes use of new algorithms for control, estimation and learning of stochastic systems, which are presented in this book.