A stabilizing min-max generalized predictive control

Presents a min-max generalized predictive control (MMGPC) which is robust to disturbance and has guaranteed stability. The MMGPC is derived from the min-max problem possessing non-recursive forms which do not use the Riccati equations. The stability conditions of the proposed control law, satisfied by changing parameters such as input-output weightings, are presented. A systematic way using the LMI (linear matrix inequality) method is presented to obtain appropriate parameters for these conditions. It is also shown that the suggested control guarantees that the induced norm from disturbances to system outputs is bounded within a constant value under the same stability conditions