工业大系统双层结构预测控制的集中优化与分散控制策略

邹涛; 魏峰; 张小辉

doi:10.3724/SP.J.1004.2013.01366

工业大系统双层结构预测控制的集中优化与分散控制策略

doi: 10.3724/SP.J.1004.2013.01366

1.
中国科学院沈阳自动化研究所沈阳 110016;
2.
浙江工业大学信息工程学院杭州 310023;
3.
新奥智能能源有限公司廊坊 065001

基金项目:

国家自然科学基金(61074059);中国科学院知识创新项目(KGCX2-EW-104);浙江省科技厅公益项目(2011c31040);浙江省教育厅科研项目(Y201121651);河北省应用基础研究计划重点基础研究项目(13964509D)资助

详细信息

作者简介:
魏峰浙江工业大学信息工程学院硕士研究生. 主要研究方向为大系统双层结构预测控制. E-mail: wf5552682@126.com

计量
- 文章访问数: 1744
- HTML全文浏览量: 88
- PDF下载量: 1691
- 被引次数: 0
出版历程
- 收稿日期: 2012-05-13
- 修回日期: 2012-07-22
- 刊出日期: 2013-08-20

Strategy of Centralized Optimization and Decentralized Control for Two-layered Predictive Control in Large-scale Industrial Systems

1.
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016;
2.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023;
3.
ENN Intelligent Energy Group, Langfang 065001

Funds:

Supported by National Natural Science Foundation of China (61074059), the Innovation Key Program (KGCX2-EW-104) of Chinese Academy of Sciences, the Public Welfare Project from the Science Technology Department of Zhejiang Province (2011c31040), Scientific Research Fund of Zhejiang Provincial Education Department (Y201121651), and the Key Basic Research Project of Application Basic Research of Hebei Province (13964509D)

摘要

摘要: 为降低工业大系统模型预测控制(Model predictive control,MPC)在线计算复杂度,同时保证系统的全局优化性能,提出一种集中优化、分散控制的双层结构预测控制策略.在稳态目标计算层(Steady-state target calculation, SSTC),基于全局过程模型对系统进行集中优化,将优化结果作为设定值传递给动态控制层;在动态控制层,将大系统划分为若干个子系统,每个子系统分别由基于各自子过程模型的模型预测控制进行控制,为减少各子系统之间的相互干扰,在各个子系统之间添加前馈控制器对扰动进行补偿,提高系统的总体动态控制性能.该策略的优点在于能确保系统全局最优性的同时降低了在线计算量,提高了工业大系统双层结构预测控制方法的实时性.仿真实例验证该方法的有效性.
- 预测控制 /
- 子系统 /
- 前馈控制器 /
- 双层结构 /
- 大系统
Abstract: Aiming at reducing the on-line computational burden of model predictive control (MPC) for large-scale systems and maintaining the global optimization performance, a new two-layered MPC strategy is proposed. In the steady-state target calculation (SSTC) layer, the centralized optimization of the system is based on the global process model. The optimal results are sent to the dynamic control layer as the set points. In dynamic control layer, the large-scale system is divided into several subsystems which will be controlled with separate MPCs. To compensate the interference between the subsystems, feedforward controllers are added between the subsystems, so that the overall dynamic control performance of the system will be improved. The advantages of the proposed strategy are that, the global optimal performance of the system is guaranteed, while the on-line computational complexity is reduced, and the realtime property of the two-layered MPC for large industrial system is enhanced. Simulation examples are given to verify the proposed method.
- Predictive control /
- subsystem /
- feed-forward controller /
- two-layered structure /
- large-scale system

HTML全文

参考文献(16)

[1]	Wang Y, Boyd S P. Fast model predictive control using online optimization. IEEE Transactions on Control Systems Technology, 2010, 18(2): 267-278
[2]	Diehl M, Amrit R, Rawlings J B. A Lyapunov function for economic optimizing model predictive control. IEEE Transactions on Automatic Control, 2011, 56(3): 703-707
[3]	Ling K V, Maciejowski J, Richards A, Wu B F. Multiplexed model predictive control. Automatica, 2012, 48(2): 396-401
[4]	Cao Z W, Easterling D R, Watson L T, Li D, Cameron K W, Feng WC. Power saving experiments for large-scale global optimisation. International Journal of Parallel, Emergent, and Distributed Systems, 2010, 25(5): 381-400
[5]	Qin S J, Badgwell T A. A survey of industrial model predictive control technology. Control Engineering Practice, 2003, 11(7): 733-764
[6]	Kassmann D E, Badgwell T A, Hawkins R B. Robust steady-state target calculation for model predictive control. AIChE Journal, 2000, 46(5): 1007-1024
[7]	NijandrovI A, Swartz C L E. Sensitivity analysis of LP-MPC cascade control systems. Journal of Process Control, 2009, 19(1): 16-24
[8]	Xi Yu-Geng. Introduction of Dynamic Large-Scale Systems. Beijing: National Defense Press, 1988 (席裕庚. 动态大系统方法导论. 北京: 国防工业出版社, 1988)
[9]	Adetola V, Guay M. Integration of real-time optimization and model predictive control. Journal of Process Control, 2010, 20(2): 125-133
[10]	Zhang Y, Li S Y. Networked model predictive control based on neighbourhood optimization for serially connected large-scale processes. Journal of Process Control, 2007, 17(1): 37-50
[11]	Venkat A N, Rawlings J B, Wright S J. Stability and optimality of distributed model predictive control. In: Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference. Seville, Spain: IEEE, 2005. 6680-6685
[12]	Scattolini R. Architectures for distributed and hierarchical model predictive control—a review. Journal of Process Control, 2009, 19(5): 723-731
[13]	Zou Tao, Ding Bao-Cang, Zhang Duan. Model Predictive Control Engineering Application Introduction. Beijing: Chemical Industrial Press, 2010. 69-83(邹涛, 丁宝苍, 张端. 模型预测控制工程应用导论. 北京: 化学工业出版社, 2010. 69-83)
[14]	Qian Ji-Xin, Zhao Jun, Xu Zu-Hua. Predictive Control. Beijing: Chemical Industry Press, 2007(钱积新, 赵均, 徐祖华. 预测控制. 北京: 化学工业出版社, 2007)
[15]	Ding Bao-Cang. The Predictive Control Theory and Methods. Beijing: China Machine Press, 2008(丁宝苍. 预测控制的理论与方法. 北京: 机械工业出版社, 2008)
[16]	Yuan Wei-Ping, Sun Zhi-Zhong, Wu Hong-Wei, Wen Zhen-Chu. Computing Methods and Laboratory. Nanjing: Southeast University Press, 2005(袁慰平, 孙志忠, 吴宏伟, 闻震初. 计算方法与实习. 南京: 东南大学出版社, 2005)