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

被引：2

作者：

Hou, Ligang ^{[1
]}

Wu, Ze ^{[2
]}

Jin, Xin ^{[1
]}

Wang, Yue ^{[1
]}

机构：

[1] Liaoning Shihua Univ, Sch Informat & Control Engn, Fushun 113001, Peoples R China

[2] Petrochina West Pipeline Co, Dushanzi Oil & Gas Transportat Branch Co, Karamay 833699, Peoples R China

来源：

INFORMATION | 2019年 / 10卷 / 01期

关键词：

partial least square; model predictive control; state space model; offset-free control; PARTIAL LEAST-SQUARES; RELEVANT IDENTIFICATION; SYSTEMS;

D O I：

10.3390/info10010005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：22

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