Design of Robust Predictive Control Laws Using Set Membership Identified Models

This paper investigates the robust design of nonlinear model predictive control (NMPC) laws that employ approximated models, derived directly from process input‐output data. In particular, a nonlinear set membership (NSM) identification technique is used to obtain a system model and a bound of the r...

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Published in:Asian journal of control 2013-11, Vol.15 (6), p.1714-1722
Main Authors: Canale, M., Fagiano, L., Signorile, M.C.
Format: Article
Language:English
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Controllers
Mathematical models
nonlinear control
Predictive control
robust stability
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description This paper investigates the robust design of nonlinear model predictive control (NMPC) laws that employ approximated models, derived directly from process input‐output data. In particular, a nonlinear set membership (NSM) identification technique is used to obtain a system model and a bound of the related uncertainty. The latter is used to carry out a robust control design, via a min‐max formulation of the optimal control problem underlying the NMPC methodology. A numerical example with a nonlinear oscillator shows the effectiveness of the proposed approach.
doi_str_mv 10.1002/asjc.560
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subjects Controllers
Mathematical models
nonlinear control
Predictive control
robust stability
title Design of Robust Predictive Control Laws Using Set Membership Identified Models
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