基于多优先级稳态优化的双层结构预测控制算法及软件实现

潘红光; 高海南; 孙耀; 张英; 丁宝苍

doi:10.3724/SP.J.1004.2014.00405

基于多优先级稳态优化的双层结构预测控制算法及软件实现

doi: 10.3724/SP.J.1004.2014.00405

1.
西安交通大学智能网络与网络安全教育部重点实验室电子与信息工程学院自动化系西安 710049

基金项目:

国家自然科学基金（61174095），工业控制技术国家重点实验室（ICT1213）资助

详细信息

作者简介:
潘红光西安交通大学博士研究生. 主要研究方向为预测控制.E-mail：hongguangpan@163.com

通讯作者:
丁宝苍

计量
- 文章访问数: 2266
- HTML全文浏览量: 89
- PDF下载量: 1159
- 被引次数: 0
出版历程
- 收稿日期: 2012-12-24
- 修回日期: 2013-05-23
- 刊出日期: 2014-03-20

The Algorithm and Software Implementation for Double-layered Model Predictive Control Based on Multi-priority Rank Steady-state Optimization

1.
Key Laboratory for Intelligent Networks and Network Security, Ministry of Education (MOE KLINNS), Department of Automation, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049

Funds:

Supported by National Natural Science Foundation of China (61174095) and the State Key Laboratory of Industrial Control Technology (ICT1213)

摘要

摘要: 研究包含稳态目标计算（Steady-state target calculation，SSTC）层和动态控制层的双层结构预测控制（Model predictive control，MPC）及其实现方法. 我们将已有的辨识、优化和控制方案适当地组合并软件化. 通过在多优先级稳态目标计算中引入新的变量，给出了稳态目标计算的统一表达方法，每个优先级的优化问题或是跟踪外部目标，或是放松软约束. 通过仿真算例和应用实例相结合的方式验证了软件功能.
- 预测控制 /
- 多优先级策略 /
- 稳态目标计算 /
- 动态控制
Abstract: This paper studies the model predictive control (MPC) with multi-priority rank steady-state target calculation (SSTC) and dynamic control, and its implementation method. We properly combine the methods of identification, optimization and control in the existing literature, and implement these methods by software. By introducing new variables, the SSTC can be represented in a unified method, and the optimization problem in each priority rank is either the external target tracking or the soft constraints softening. We verify the software functions of the multi-priority rank SSTC via simulation and application examples.
- Model predictive control (MPC) /
- multi-priority rank strategy /
- steady-state target calculation (SSTC) /
- dynamic control

HTML全文

参考文献(24)

[1]	Xi Yu-Geng, Li De-Wei. Fundamental philosophy and status of qualitative synthesis of model predictive control. Acta Automatica Sinica, 2008, 34(10): 1225-1234 (席裕庚, 李德伟. 预测控制定性综合理论的基本思路和研究现状. 自动化学报, 2008, 34(10): 1225-1234)
[2]	Ding Bao-Cang. The Theories and Methods of Model Predictive Control. 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)
[3]	Zou Tao. The Steady State Optimization and Dynamic Control of Constrained Multivariable Systems [Ph.D. dissertation], Shanghai Jiao Tong University, China, 2007 (邹涛. 多变量有约束控制系统的稳态优化与动态控制 [博士学位论文], 上海交通大学, 中国, 2007)
[4]	Xu Zu-Hua. Research on Theory and Applications of Model Predictive Control [Ph.D. dissertation], Zhejiang University, China, 2004 (徐祖华. 模型预测控制理论及应用研究 [博士学位论文], 浙江大学, 中国, 2004)
[5]	[5] Skogestad S. Plantwide control: the search for the self-optimizing control structure. Journal of Process Control, 2000, 10(5): 487-507
[6]	[6] Qin S J, Badgwell T A. A survey of industrial model predictive control technology. Control Engineering Practice, 2003, 11(7): 733-764
[7]	[7] Rao C V, Rawlings J B. Steady states and constraints in model predictive control. AIChE Journal, 1999, 45(6): 1266-1278
[8]	[8] Ying C M, Joseph B. Performance and stability analysis of LP-MPC and QP-MPC cascade control systems. AIChE Journal, 1999, 45(7): 1521-1534
[9]	[9] 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
[10]	Nikandrov A, Swartz C L E. Sensitivity analysis of LP-MPC cascade control systems. Journal of Process Control, 2009, 19(1): 16-24
[11]	Pannocchia G, Kerrigan E C. Offset-free receding horizon control of constrained linear systems. AIChE Journal, 2005, 51(12): 3134-3146
[12]	Maeder U, Morari M. Offset-free reference tracking with model predictive control. Automatica, 2010, 46(9): 1469-1476
[13]	Maeder U, Borrelli F, Morari M. Linear offset-free model predictive control. Automatica, 2009, 45(10): 2214-2222
[14]	Muske K R, Badgwell T A. Disturbance modeling for offset-free linear model predictive control. Journal of Process Control, 2002, 12(5): 617-632
[15]	Van Overschee P, De Moor B L. Subspace Identification for Linear Systems: Theory, Implementation, Applications. Norwell, MA: Kluwer, 1996
[16]	Katayama T. Subspace Methods for System Identification. Germany: Springer, 2005
[17]	Qin S J. An overview of subspace identification. Computers and Chemical Engineering, 2006, 30(10-12): 1502-1513
[18]	Huang B, Kadali R. Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach. Germany: Springer-Verlag, 2008
[19]	Zou T, Li H Q, Zhang X X, Su H Y. Feasibility and soft constraint of steady state target calculation layer in LP-MPC and QP-MPC cascade control systems. In: Proceedings of 2011 International Symposium on Advanced Control of Industrial Processes (ADCONIP). Hangzhou, China: IEEE, 2011. 524-529
[20]	Feng Shao-Hui. Research on Key Technology and Applications of Model Predictive Control Software [Ph.D. dissertation], Zhejiang University, China, 2003 (冯少辉. 模型预测控制工程软件关键技术及应用研究 [博士学位论文], 浙江大学, 中国, 2003)
[21]	Liu Fu-Chun. Research and Applications of Multi-variable Constrained Model Predictive Control Algorithm and Software [Ph.D. dissertation], Zhejiang University, China, 2003 (刘富春.多变量有约束模型预测控制算法及软件实现研究与应用[博士学位论文], 浙江大学, 中国, 2003)
[22]	Ding B C, Zou T, Pan H G. A discussion on stability of offset-free linear model predictive control. In: Proceedings of the 2012 24th Chinese Control and Decision Conference (CCDC). Taiyuan, China: IEEE, 2012. 80-85
[23]	Li S Y, Zheng Y, Wang B P. Steady-state target calculation for constrained predictive control systems based on goal programming. Asia-Pacific Journal of Chemical Engineering, 2008, 3(6): 648-655
[24]	Zhu Yu-Cai. Multivariable System Identification for Process Control. Changsha: National University of Defense Science and Technology Press, 2005 (朱豫才. 过程控制的多变量系统辨识. 长沙: 国防科技大学出版社, 2005)