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Information 2019, 10(1), 5; https://doi.org/10.3390/info10010005

Linear Offset-Free Model Predictive Control in the Dynamic PLS Framework

1, 2, 1,* and 1
1
School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China
2
Dushanzi Oil and Gas Transportation Branch Company, Petrochina West Pipeline Company, Karamay 833699, China
*
Author to whom correspondence should be addressed.
Received: 8 November 2018 / Revised: 14 December 2018 / Accepted: 17 December 2018 / Published: 24 December 2018
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Abstract

This work addresses the model predictive control (MPC) of the offset-free tracking problem in the dynamic partial least square (DyPLS) framework. Firstly, state space MPC based on the DyPLS is proposed. Then, two methods are proposed to solve the offset-free problem. One is to reform the state space model as a velocity form. Another is to augment the state space model with a disturbance model and estimate the mismatch between system output and model output with an estimator. Both methods use the system output as a feedback in the control scheme. Hence, the offset-free tracking is guaranteed, and unmeasured step disturbance can be rejected. The results of two simulations demonstrate the effectiveness of proposed methods. View Full-Text
Keywords: partial least square; model predictive control; state space model; offset-free control partial least square; model predictive control; state space model; offset-free control
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hou, L.; Wu, Z.; Jin, X.; Wang, Y. Linear Offset-Free Model Predictive Control in the Dynamic PLS Framework. Information 2019, 10, 5.

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