Robustly stable feedback min-max model predictive control
Kerrigan E. C. and Maciejowski J. M.
Proc. American Control Conference, June 2003Abstract
This paper is concerned with the practical real-time implementability of robustly stable model predictive control (MPC) when constraints are present on the inputs and the states. We assume that the plant model is known, is discrete-time and linear time-invariant, is subject to unknown but bounded state disturbances and that the states of the system are measured. In this paper we introduce a new stage cost and show that the use of this cost allows one to formulate a robustly stable MPC problem that can be solved using a single linear program. Furthermore, this is a multi-parametric linear program, which implies that the receding horizon control (RHC) law is piecewise affine, and can be explicitly pre-computed, so that the linear program does not have to be solved on-line.
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BibTex Entry
- @InProceedings{kerrigan:maciejowski:2003a,
- author = {Kerrigan E. C. and Maciejowski J. M.},
- title = {Robustly stable feedback min-max model predictive control},
- address = {Denver, Colorado, USA},
- booktitle = {Proc. American Control Conference},
- bibkey = {kerrigan:maciejowski:2003a},
- month = {June},
- year = {2003}
- }
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