  This paper presents a computationally efficient robust model predictive
control law for discrete linear time invariant systems subject to additive
disturbances that may depend on the state and/or input norms. Despite the
dependency being non-convex, we are able to capture it exactly for input
dependency and approximately for state dependency in at most a second order
cone programming problem. The formulation has linear complexity in the planning
horizon length. The approach is thus amenable to efficient real-time
implementation with a guarantee on recursive feasibility and global optimality.
Robust position control of a satellite is considered as an illustrative
example.
