Abstract
The practicality of model predictive control (MPC) is partially limited by its ability to solve optimization problems in real time. Moreover, on-line computational demand for synthesizing a robust MPC algorithm will likely grow significantly with the problem size. In this paper, we use the concept of an asymptotically stable invariant ellipsoid to develop a robust constrained MPC algorithm which gives a sequence of explicit control laws corresponding to a sequence of asymptotically stable invariant ellipsoids constructed off-line one within another in state space. This off-line approach can address a broad class of model uncertainty descriptions with guaranteed robust stability of the closed-loop system and substantial reduction of the on-line MPC computation. The controller design is illustrated with two examples.
Original language | English (US) |
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Pages (from-to) | 837-846 |
Number of pages | 10 |
Journal | Automatica |
Volume | 39 |
Issue number | 5 |
DOIs | |
State | Published - May 2003 |
Externally published | Yes |
Keywords
- Asymptotic stability
- Invariant ellipsoid
- Linear matrix inequalities
- Model predictive control
- Multivariable constrained systems
- On-line computation
- Robust stability
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering