Explicit solution of min–max MPC with additive uncertainties and quadratic criterion

Min–max model predictive control (MMMPC) is one of the strategies proposed to control plants subject to bounded uncertainties. This technique is very difficult to implement in real time because of the computation time required. Recently, the piecewise affine nature of this control law has been prove...

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Bibliographic Details
Published in:Systems & control letters 2006-04, Vol.55 (4), p.266-274
Main Authors: Muñoz de la Peña, D., Ramírez, D.R., Camacho, E.F., Alamo, T.
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control system synthesis
Control theory. Systems
Exact sciences and technology
Predictive control
Robust control
Uncertain systems
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Summary:Min–max model predictive control (MMMPC) is one of the strategies proposed to control plants subject to bounded uncertainties. This technique is very difficult to implement in real time because of the computation time required. Recently, the piecewise affine nature of this control law has been proved for unconstrained linear systems with quadratic performance criterion. However, no algorithm to compute the explicit form of the control law was given. This paper shows how to obtain this explicit form by means of a constructive algorithm. An approximation to MMMPC in the presence of constraints is presented based on this algorithm.
ISSN:0167-6911
1872-7956
DOI:10.1016/j.sysconle.2005.08.006