Nonlinear model predictive control with enlarged terminal sets using support vector machine

In this paper, model predictive control (MPC) of nonlinear systems subject to input and state constraints is considered, for which nominal closed-loop stability is guaranteed. We propose the use of a large terminal invariant set and an estimate of the terminal cost to reduce the online computational...

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Bibliographic Details
Published inIFAC Proceedings Volumes Vol. 37; no. 13; pp. 573 - 578
Main Authors Sui, Dan, Ong, Chong Jin, Keerthi, S Sathiya
Format Journal Article
LanguageEnglish
Published 01.09.2004
Subjects
Constraints
Model predictive control
Stability
Terminal conditions
Model predictive control
Constraints
Stability
Terminal conditions
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Summary:In this paper, model predictive control (MPC) of nonlinear systems subject to input and state constraints is considered, for which nominal closed-loop stability is guaranteed. We propose the use of a large terminal invariant set and an estimate of the terminal cost to reduce the online computational burden of MPC. These terminal sets and costs are learned off-line via support vector machine method. Its main advantage with respect to other well-known techniques is the reduction of online computational effort by relaxing the terminal constraints. An example illustrates the efficiency of the approach.
ISSN:1474-6670
DOI:10.1016/S1474-6670(17)31285-5