  A robust model predictive control (MPC) method is presented for linear,
time-invariant systems affected by bounded additive disturbances. The main
contribution is the offline design of a disturbance-affine feedback gain
whereby the resulting constraint tightening is minimized. This is achieved by
formulating the constraint tightening problem as a convex optimization problem
with the feedback term as a variable. The resulting MPC controller has the
computational complexity of nominal MPC, and guarantees recursive feasibility,
stability and constraint satisfaction. The advantages of the proposed approach
compared to existing robust MPC methods are demonstrated using numerical
examples.
