  Robust design of autonomous systems under uncertainty is an important yet
challenging problem. This work proposes a robust controller that consists of a
state estimator and a tube based predictive control law. The class of linear
systems under ellipsoidal uncertainty is considered. In contrast to existing
approaches based on polytopic sets, the constraint tightening is directly
computed from the ellipsoidal sets of disturbances without over-approximation,
thus leading to less conservative bounds. Conditions to guarantee robust
constraint satisfaction and robust stability are presented. Further, by
avoiding the usage of Minkowski sum in set computation, the proposed approach
can also scale up to high-dimensional systems. The results are illustrated by
examples.
