4.5 Article

Robust nonlinear MPC based on Volterra series and polynomial chaos expansions

Journal

JOURNAL OF PROCESS CONTROL
Volume 24, Issue 1, Pages 304-317

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2013.03.003

Keywords

Nonlinear model predictive control; Robust control; Volterra series; Structured Singular Value; Polynomial chaos expansions

Funding

  1. NSERC (National Sciences and Engineering Research Council of Canada)

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A robust nonlinear model predictive controller (NMPC) based on a Volterra series is proposed. Polynomial chaos expansions (PCE) are used to represent the uncertainty in the Volterra series coefficients and this uncertainty is then propagated onto the output predictions. The key advantage of the PCE is that it provides an analytical expression to compute the L-2-norm of the output prediction error resulting in computational savings, compared to previously proposed techniques, which are essential for real time implementation. Terminal and input constraints based on Structured Singular Value based-norms are used to ensure convergence to a set-point and compliance with constraints in manipulated variables. The algorithm is applied to a multivariable pH neutralization system. A comparative study shows superior closed loop performance and computational efficiency of the proposed technique as compared to previously proposed algorithms. (C) 2013 Elsevier Ltd. All rights reserved.

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