Piecewise affinity of min–max MPC with bounded additive uncertainties and a quadratic criterion

This brief shows how a min–max MPC with bounded additive uncertainties and a quadratic cost function results in a piecewise affine and continuous control law. Proofs based on properties of the cost function and the optimization problem are given. The boundaries of the regions in which the state spac...

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Bibliographic Details
Published in:Automatica (Oxford) 2006-02, Vol.42 (2), p.295-302
Main Authors: Ramirez, D.R., Camacho, E.F.
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control system analysis
Control theory. Systems
Exact sciences and technology
Min–max MPC algorithm
Min–max optimization
Miscellaneous
Piecewise linear controllers
Predictive control
Uncertainty
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Summary:This brief shows how a min–max MPC with bounded additive uncertainties and a quadratic cost function results in a piecewise affine and continuous control law. Proofs based on properties of the cost function and the optimization problem are given. The boundaries of the regions in which the state space can be partitioned are also treated. The results are illustrated by an example.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2005.09.009