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Information 2017, 8(4), 121; doi:10.3390/info8040121

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China
Author to whom correspondence should be addressed.
Received: 30 August 2017 / Revised: 23 September 2017 / Accepted: 25 September 2017 / Published: 10 October 2017
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This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS) framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC) controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method. View Full-Text
Keywords: partial least square; model predictive control; offset free control; distributed controller partial least square; model predictive control; offset free control; distributed controller

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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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Xin, J.; Yue, W.; Lin, L. Offset Free Tracking Predictive Control Based on Dynamic PLS Framework. Information 2017, 8, 121.

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