Computational burden reduction in min–max MPC

Min–max model predictive control (MMMPC) is one of the strategies used to control plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the complex numerical optimization problem that has to be solved at every sampling time. This paper shows...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2011-11, Vol.348 (9), p.2430-2447
Main Authors: Ramirez, D.R., Alamo, T., Camacho, E.F.
Format: Article
Language:English
Subjects:
Applied sciences
Computation
Computer science
control theory
systems
Computer simulation
Control system synthesis
Control theory. Systems
Cost function
Exact sciences and technology
Mathematical models
Optimal control
Optimization
Sampling
Strategy
Uncertainty
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Summary:Min–max model predictive control (MMMPC) is one of the strategies used to control plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the complex numerical optimization problem that has to be solved at every sampling time. This paper shows how to overcome this by transforming the original problem into a reduced min–max problem whose solution is much simpler. In this way, the range of processes to which MMMPC can be applied is considerably broadened. Proofs based on the properties of the cost function and simulation examples are given in the paper.
ISSN:0016-0032
1879-2693
DOI:10.1016/j.jfranklin.2011.07.008