Stabilizable regions of receding horizon predictive control with input constraints

✍ Scribed by Y.I. Lee; B. Kouvaritakis

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 128 KB
• Volume: 38
• Category: Article
• ISSN: 0167-6911
• DOI: 10.1016/s0167-6911(99)00042-0

✦ Synopsis

Stabilizable regions of receding horizon predictive control (RHPC) with input constraints are examined. A feasible region of states, which is spanned by eigenvectors of the closed-loop system with a stabilizing feedback gain, is derived in conjunction with input constraints. For states in this region, the feasibility of state feedback is guaranteed with the corresponding feedback gain. It is shown that an RHPC scheme with adequate ÿnite terminal weights can guarantee stability for any initial state which can be steered into this region using ÿnite number of control moves in the presence of input saturation. This methodology results in feasible regions which are inÿnite (in certain directions) even in the case of open-loop unstable systems. It is shown that the proposed feasible regions are larger than the ellipsoidal regions which were suggested in earlier works. We formulated the optimization problem in LMI so that it can be solved by semideÿnite programming.