article

A General Robust MPC Design for the State-Space Model: Application to Paper Machine Process

Authors:

S. Hassan HosseinNia,

Michael LundhAuthors Info & Claims

Asian Journal of Control, Volume 18, Issue 5

Pages 1891 - 1907

https://doi.org/10.1002/asjc.1252

Published: 01 September 2016 Publication History

Abstract

Applying model predictive control MPC in some cases such as complicated process dynamics and/or rapid sampling leads us to poorly numerically conditioned solutions and heavy computational load. Furthermore, there is always mismatch in a model that describes a real process. Therefore, in this paper in order to prevail over the mentioned difficulties, we design a robust MPC using the Laguerre orthonormal basis in order to speed up the convergence at the same time with lower computation adding an extra parameter "a" in MPC. In addition, the Kalman state estimator is included in the prediction model and accordingly the MPC design is related to the Kalman estimator parameters as well as the error of estimations which helps the controller react faster against unmeasured disturbances. Tuning the parameters of the Kalman estimator as well as MPC is another achievement of this paper which guarantees the robustness of the system against the model mismatch and measurement noise. The sensitivity function at low frequency is minimized to tune the MPC parameters since the lower the magnitude of the sensitivity function at low frequency the better command tracking and disturbance rejection results. The integral absolute error IAE and peak of the sensitivity are used as constraints in optimization procedure to ensure the stability and robustness of the controlled process. The performance of the controller is examined via the controlling level of a Tank and paper machine processes.

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Cited By

Zhang YZheng YLi SZou Y(2021)Economic model predictive control for the operation optimization of water distribution networks with risksAsian Journal of Control10.1002/asjc.221823:1(128-142)Online publication date: 25-Jan-2021
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Zhao J(2018)Neural Network Predictive Control for Autonomous Underwater Vehicle with Input DelayJournal of Control Science and Engineering10.1155/2018/23169572018Online publication date: 29-May-2018
https://dl.acm.org/doi/10.1155/2018/2316957

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cover image Asian Journal of Control

Asian Journal of Control Volume 18, Issue 5

September 2016

366 pages

ISSN:1561-8625

EISSN:1934-6093

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John Wiley & Sons, Inc.

United States

Publication History

Published: 01 September 2016

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Cited By

Zhang YZheng YLi SZou Y(2021)Economic model predictive control for the operation optimization of water distribution networks with risksAsian Journal of Control10.1002/asjc.221823:1(128-142)Online publication date: 25-Jan-2021
https://dl.acm.org/doi/10.1002/asjc.2218
Zhao J(2018)Neural Network Predictive Control for Autonomous Underwater Vehicle with Input DelayJournal of Control Science and Engineering10.1155/2018/23169572018Online publication date: 29-May-2018
https://dl.acm.org/doi/10.1155/2018/2316957

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