Optimal control of grinding mill circuit using model predictive static programming : a new nonlinear MPC paradigm

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dc.contributor.author Le Roux, Johan Derik
dc.contributor.author Padhi, Radhakant
dc.contributor.author Craig, Ian Keith
dc.date.accessioned 2015-02-25T12:52:16Z
dc.date.available 2015-02-25T12:52:16Z
dc.date.issued 2014-12
dc.description.abstract The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a ‘new paradigm’ under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants. en_ZA
dc.description.librarian hj2015 en_ZA
dc.description.uri http://www.elsevier.com/locate/jprocont en_ZA
dc.identifier.citation Le Roux, JD, Padhi, R & Craig, IK 2014, 'Optimal control of grinding mill circuit using model predictive static programming : a new nonlinear MPC paradigm', Journal of Process Control, vol. 24, no. 12, pp. 29-40. en_ZA
dc.identifier.issn 0959-1524 (print)
dc.identifier.issn 1873-2771 (online)
dc.identifier.other 10.1016/j.jprocont.2014.10.007
dc.identifier.uri http://hdl.handle.net/2263/43822
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2014 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Journal of Process Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Process Control, vol. 24, no. 12, pp. 29-40, 2014. doi : 10.1016/j.jprocont.2014.10.007 en_ZA
dc.subject Model predictive static programming (MPSP) en_ZA
dc.subject Comminution en_ZA
dc.subject Grinding mill en_ZA
dc.subject Model predictive control (MPC) en_ZA
dc.subject Optimal control en_ZA
dc.title Optimal control of grinding mill circuit using model predictive static programming : a new nonlinear MPC paradigm en_ZA
dc.type Postprint Article en_ZA


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