无偏模型预测控制综述

王浩坤; 徐祖华; 赵均; 江爱朋

doi:10.16383/j.aas.c180415

无偏模型预测控制综述

doi: 10.16383/j.aas.c180415

王浩坤^1, ,,
徐祖华^2,,
赵均^2,,
江爱朋^1,

1.
杭州电子科技大学自动化学院 (人工智能学院) 杭州 310018
2.
浙江大学控制科学与工程学院杭州 310027

基金项目:

浙江省公益技术应用研究计划项目 2017C31065

NSFC-浙江两化融合联合基金 U1509209

国家重点研发计划 2017YFB0603703

国家自然科学基金 61374142

详细信息

作者简介:
徐祖华  浙江大学控制科学与工程学院副教授.主要研究方向为模型预测控制, 迭代学习控制和系统辨识.E-mail: zhxu@zju.edu.cn

赵均  浙江大学控制科学与工程学院副教授.主要研究方向为模型预测控制, 工业大数据分析.E-mail: jzhao@iipc.zju.edu.cn

江爱朋  杭州电子科技大学自动化学院教授.主要研究方向为大规模复杂过程系统的模拟, 控制与优化.E-mail: jiangaipeng@163.com

通讯作者:
王浩坤杭州电子科技大学自动化学院讲师.主要研究方向为模型预测控制及其工业应用.本文通信作者.E-mail: hkwang@hdu.edu.cn

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出版历程
- 收稿日期: 2018-06-12
- 录用日期: 2018-09-17
- 刊出日期: 2020-06-01

A Survey on Ofiset-free Model Predictive Control

WANG Hao-Kun^{1
, ,},
XU Zu-Hua^2
,,
ZHAO Jun^2
,,
JIANG Ai-Peng^1
,

1.
School of Automation (Artiflcial Intelligence), Hangzhou Dianzi University, Hangzhou 310018
2.
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027

Funds:

the Public Projects of Zhejiang Province, China 2017C31065

NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization U1509209

National Key R & D Program of China 2017YFB0603703

the National Natural Science Foundation of China 61374142

More Information

Author Bio:
XU Zu-Hua Associate professor at the College of Control Science and Engineering, Zhejiang University. His research interest covers model predictive control, iterative learning control and system identification

ZHAO Jun Associate professor at the College of Control Science and Engineering, Zhejiang University. His research interest covers model predictive control and industrial big data analysis

JIANG Ai-Peng Professor at School of Automation, Hangzhou Dianzi University. His research interest covers the simulation, control, and optimization of large scale complex process systems

Corresponding author: WANG Hao-Kun Lecturer at the School of Automation, Hangzhou Dianzi University. His research interest covers model predictive control and its application. Corresponding author of this paper

摘要

摘要: 无偏(静差)模型预测控制(Model predictive control, MPC)的设计目标是使被控变量渐近地跟踪设定值, 这类控制方法直接关系到闭环系统的跟踪性能和抗扰性能.由于可以有效处理不可测扰动、模型失配等, 无偏MPC具有很强的工程应用价值, 但是在理论方面并没有得到充分重视.近30年来, 围绕无偏MPC的原理、分析和设计展开了一系列的研究工作, 并取得了系统性的研究成果.当前的一些研究结果大多分散在不同的参考文献中, 缺少全面的梳理和呈现.本文的主要工作包括回顾常见无偏控制方法, 综述当前无偏MPC的研究进展, 并探讨一些潜在的研究方向.
- 模型预测控制 /
- 无偏控制 /
- 跟踪控制 /
- 扰动建模 /
- 扰动估计器
Abstract: The goal of offset-free MPC (Model predictive control) is to drive the controlled variables to the desired setpoints asymptotically. Due to the ability to cope with unmeasured disturbances and/or model mismatch, offset-free control strategies are directly related to the tracking performance and disturbance rejection performance. Offset-free MPC is fundamental for practical implementation, however, is often overlooked in academic researches. Great achievements have been made in the area of offset-free MPC over the last three decades. The existing results scatter in many academic papers and books, and there are few systemic discussions. This paper aims to shed some light on the theory and design of offset-free MPC, including common offset-free control strategies, current research activities, and some possible directions in the future.
- Model predictive control /
- offset-free control /
- tracking control /
- disturbance modeling /
- disturbance observer
Recommended by Associate Editor ZHU Bing
注释:

1) 本文责任编委诸兵

HTML全文

图 1 IMC系统结构

Fig. 1 Structure of IMC system

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图 2 双层MPC结构示意图

Fig. 2 Double-layered MPC

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图 3 不同扰动模型下对应的输出预测

Fig. 3 Predictive outputs with different disturbance models

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图 4 $S$对系统抗扰能力的影响(输出扰动模型+输出扰动)

Fig. 4 Disturbance rejection performance with different $S$ (output disturbance model + output disturbance)

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图 5 $S$对系统抗扰能力的影响(输入扰动模型+输入扰动)

Fig. 5 Disturbance rejection performance with different $S$ (input disturbance model + input disturbance)

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图 6 动态跟踪问题中的各种滞后

Fig. 6 Lags in the tracking control problem

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