MPC: Current practice and challenges

被引：243
作者：
Darby, Mark L. [1 ]
Nikolaou, Michael [2 ]
机构：
[1] CMiD Solut, Houston, TX 77065 USA
[2] Univ Houston, Houston, TX 77204 USA
来源：
CONTROL ENGINEERING PRACTICE | 2012年 / 20卷 / 04期
关键词：
Model predictive control; Model-based control; Constraints; Control system design; Modeling; Process identification; MODEL-PREDICTIVE CONTROL; CLOSED-LOOP IDENTIFICATION; ONLINE OPTIMIZATION; STABILITY; CONSISTENCY; SYSTEMS;
D O I：
10.1016/j.conengprac.2011.12.004
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Linear Model Predictive Control (MPC) continues to be the technology of choice for constrained, multivariable control applications in the process industry. Successful deployment of MPC requires "getting right" multiple aspects of the control problem. This includes the design of the underlying regulatory controls, design of the MPC(s), test design for model identification, model development, and dealing with nonlinearities. Approaches and techniques that are successfully applied in practice are described, including the challenges involved in ensuring a successful MPC application. Academic contributions are highlighted and suggestions provided for improving MPC. (c) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码：328 / 342
页数：15
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