Robust Execution for Stochastic Hybrid Systems

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.

Autorentext
Lars has a Ph.D. in Control and Estimation from the Massachusetts Institute of Technology, where he was supervised by Prof. Brian Williams. He has B.A. and M.Eng. degrees from the University of Cambridge, supervised by Prof. Keith Glover. He is now with the Guidance and Control Analysis Group at the NASA Jet Propulsion Laboratory.

Klappentext
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.