网络信息模式下分布式系统协调预测控制

郑毅; 李少远

doi:10.3724/SP.J.1004.2013.01778

网络信息模式下分布式系统协调预测控制

doi: 10.3724/SP.J.1004.2013.01778

郑毅^1,3,
李少远^1,2

1.
上海交通大学电子信息与电气工程学院上海 200240;
2.
信息与系统教育部重点实验室上海 200240;
3.
电力传输与功率变换控制教育部重点实验室上海 200240

基金项目:

国家重点基础研究发展计划(973计划)(2013CB035500),国家自然科学基金(61233004,61221003)和高等学校博士学科点专项科研基金(20120073130006)资助

详细信息

作者简介:
郑毅上海交通大学电子信息与电气工程学院助理研究员. 2010 年于上海交通大学获得控制理论与控制工程博士学位. 主要研究方向为工业过程的建模, 控制与优化. E-mail: yizheng@sjtu.edu.cn

计量
- 文章访问数: 2194
- HTML全文浏览量: 164
- PDF下载量: 1928
- 被引次数: 0
出版历程
- 收稿日期: 2013-08-13
- 修回日期: 2013-08-31
- 刊出日期: 2013-11-20

Networked Cooperative Distributed Model Predictive Control for Dynamic Coupling Systems

ZHENG Yi^1,3,
LI Shao-Yuan^1,2

1.
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240;
2.
Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240;
3.
Key Laboratory of Power Transmission and Power Convertor, Ministry of Education of China, Shanghai 200240

Funds:

Supported by National Basic Research Program of China (973 Program) (2013CB035500), National Natural Science Foundation of China (61233004, 61221003), and Specialized Research Fund for the Doctoral Program of Higher Education (20120073130006)

摘要

摘要: 工业系统中广泛存在一类由多个相互关联的子系统组成的大系统. 尽管分布式控制结构的性能没有集中式控制好,但由于其具有较高的灵活性和容错性,相对于集中控制更加适合控制上述系统.在保持容错性的情况下如何提高系统的整体性能是分布式控制的一个难点问题.本文提出了一种分布式预测控制(Distributed model predictive control, DMPC)方法,该方法通过在各子系统预测控制器的性能指标中加入输入变量对其下游子系统的影响的二次函数,来扩大分布式预测控制的协调度,进而在不增加网络连通度,不改变系统容错性的前提下,提高系统的性能.另外,本文给出了基于该协调策略的带输入约束的分布式预测控制器的设计方法,在初始可行的前提下,该方法相继可行并可保证系统渐近稳定.
- 预测控制 /
- 分布式系统 /
- 分布式预测控制 /
- 大系统 /
- 协调控制
Abstract: For the control of a class of large-scale systems, which consists of many individual subsystems, the distributed contort framework is more suitable because of its good flexibility and error tolerant, despite the fact that its performance is not as good as that of centralized control. How to improve the performance of the entire system and, at the same time, maintain the flexibility and error tolerance characterises under the distributed framework is an important problem. In this paper, a novel distributed model predictive control (DMPC) is proposed, which enlarges the coordinate degree of DMPC through adding a quasi-function of the impact of control input to its downstream subsystems to the performance index of each subsystem-based model predictive control (MPC). Then, the performance of the entire system is improved with unchanged network connectivity. In addition, a design method with this coordination strategy and inputs constraints is provided, which guarantees the recursive feasiblity of the designed DMPC and asymptotic stablility of the closed-loop system.
- Model predictive control (MPC) /
- distributed system /
- distributed model predictive control (DMPC) /
- large-scale system /
- cooperative control

HTML全文

参考文献(30)

[1]	Scattolini R. Architectures for distributed and hierarchical model predictive control——a review. Journal of Process Control, 2009, 19(5): 723-731
[2]	Christofides P D, Scattolini R, Muñoz de la Peña D, Liu J F. Distributed model predictive control: a tutorial review and future research directions. Computers & Chemical Engineering, 2013, 51: 21-41
[3]	Min Hai-Bo, Liu Zhi-Guo, Liu Yuan, Wang Shi-Cheng, Yang Yan-Li. Coordination control of networked Euler-Lagrange systems with possible switching topology. Acta Automatica Sinica, 2013, 39(7): 1003-1010 (闵海波, 刘志国, 刘源, 王仕成, 杨艳丽. 切换网络拓扑下Euler-Lagrange系统的分布式协调控制. 自动化学报, 2013, 39(7): 1003-1010)
[4]	Chai Tian-You, Li Shao-Yuan, Wang Hong. Networked modeling and contorl for complex industrial processes. Acta Automatica Sinica, 2013, 39(5): 469-470 (柴天佑, 李少远, 王宏. 网络信息模式下复杂工业过程建模与控制. 自动化学报, 2013, 39(5): 469-470)
[5]	Xi Yu-Geng. Model Predictive Control. Beijing: National Defence Industry Press, 1993 (席裕庚. 预测控制. 北京: 国防工业出版社, 1993)
[6]	Qin S J, Badgwell T A. A survey of industrial model predictive control technology. Control Engineering Practice, 2003, 11(7): 733-764
[7]	Yang Jian, Xi Yu-Geng, Zhang Zhong-Jun. Time decomposition for moving horizon optimization in predictive control. Acta Automatica Sinica, 1995, 21(5): 555-561 (杨健, 席裕庚, 张钟俊. 预测控制滚动优化的时间分解方法. 自动化学报, 1995, 21(5): 555-561)
[8]	Kong Xiao-Bing, Liu Xiang-Jie. Nonlinear model predictive control for DFIG-based wind power generation. Acta Automatica Sinica, 2013, 39(5): 636-643 (孔小兵, 刘向杰. 双馈风力发电机非线性模型预测控制. 自动化学报, 2013, 39(5): 636-643)
[9]	Camponogara E, Jia D, Krogh B H, Talukdar S. Distributed model predictive control. IEEE Control Systems, 2002, 22(1): 44-52
[10]	Li Shao-Yuan. Model Predictive Control for Global Operating Condition System and Its Implementation. Beijing: Science Press, 2008 (李少远. 全局工况系统预测控制及其应用. 北京: 科学出版社, 2008)
[11]	Doan M D, Keviczky T, De Schutter B. An iterative scheme for distributed model predictive control using Fenchel's duality. Journal of Process Control, 2011, 21(5): 746-755
[12]	Al-Gherwi W, Budman H, Elkamel A. A robust distributed model predictive control based on a dual-mode approach. Computers & Chemical Engineering, 2013, 50: 130-138
[13]	Maestre J M, Muñoz de la Peña D, Camacho E F, Alamo T. Distributed model predictive control based on agent negotiation. Journal of Process Control, 2011, 21(5): 685-697
[14]	Scheu H, Marquardt W. Sensitivity-based coordination in distributed model predictive control. Journal of Process Control, 2011, 21(5): 715-728
[15]	Al-Gherwi W, Budman H, Elkamel A. Selection of control structure for distributed model predictive control in the presence of model errors. Journal of Process Control, 2010, 20(3): 270-284
[16]	Al-Gherwi W, Budman H, Elkamel A. A robust distributed model predictive control algorithm. Journal of Process Control, 2011, 21(8): 1127-1137
[17]	Morosan P D, Bourdais R, Dumur D, Buisson J. A distributed MPC strategy based on Benders' decomposition applied to multi-source multi-zone temperature regulation. Journal of Process Control, 2011, 21(5): 729-737
[18]	Zheng Y, Li S Y, Wang X B. Distributed model predictive control for plant-wide hot-rolled strip laminar cooling process. Journal of Process Control, 2009, 19(9): 1427-1437
[19]	Mc Namara P, Negenborn R R, De Schutter B, Lightbody G. Optimal coordination of a multiple HVDC link system using centralized and distributed control. IEEE Transactions on Control Systems Technology, 2013, 21(2): 302-314
[20]	Dunbar W B, Murray R M. Distributed receding horizon control for multi-vehicle formation stabilization. Automatica, 2006, 42(4): 549-558
[21]	Farina M, Scattolini R. Distributed predictive control: a non-cooperative algorithm with neighbor-to-neighbor communication for linear systems. Automatica, 2012, 48(6): 1088-1096
[22]	Li S Y, Zhang Y, Zhu Q M. Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem. Information Sciences, 2005, 170(2-4): 329-349
[23]	Stewart B T, Venkat A N, Rawlings J B, Wright S J, Pannocchia G. Cooperative distributed model predictive control. Systems & Control Letters, 2010, 59(8): 460-469
[24]	Zheng Y, Li S Y, Qiu H. Networked coordination-based distributed model predictive control for large-scale system. IEEE Transactions on Control Systems Technology, 2013, 21(3): 991-998
[25]	Zheng Y, Li S. The stabilization of coordinated distributed model predictive control. In: Proceedings of the 13th IFAC Symposium on Large Scale Complex Systems: Theory and Applications. Shanghai, China: IFAC, 2013
[26]	Zheng Y, Li S Y, Li N. Distributed model predictive control over network information exchange for large-scale systems. Control Engineering Practice, 2011, 19(7): 757-769
[27]	Zheng Y, Li S Y, Wu J, Zhang X X. Stabilized neighborhood optimization based distributed model predictive control for distributed system. In: Proceedings of the 31st Chinese Control Conference. Hefei, China: IEEE, 2012. 4212-4217
[28]	Zheng Y, Li S Y, Wang X B. Optimization target resetting distributed model predictive control for accelerated cooling process. In: Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, China: IEEE, 2012. 2764-2769
[29]	Alvarado I, Limon D, Muñoz de la Peña D, Maestre J M, Ridao M A, Scheu H, Marquardtb W, Negenbornc R R, De Schutterc B, Valenciad F, Espinosad J. A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark. Journal of Process Control, 2011, 21(5): 800-815
[30]	Mayne D Q, Rawlings J B, Rao C V, Scokaert P O M. Constrained model predictive control: stability and optimality. Automatica, 2000, 36(6): 789-814

施引文献

资源附件(0)

访问统计

计量

文章访问数: 2194
HTML全文浏览量: 164
PDF下载量: 1928
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

网络信息模式下分布式系统协调预测控制

doi: 10.3724/SP.J.1004.2013.01778

作者简介:
郑毅上海交通大学电子信息与电气工程学院助理研究员. 2010 年于上海交通大学获得控制理论与控制工程博士学位. 主要研究方向为工业过程的建模, 控制与优化. E-mail: yizheng@sjtu.edu.cn

计量

Networked Cooperative Distributed Model Predictive Control for Dynamic Coupling Systems

计量

目录

留言板

网络信息模式下分布式系统协调预测控制

doi: 10.3724/SP.J.1004.2013.01778

作者简介: 郑毅 上海交通大学电子信息与电气工程学院助理研究员. 2010 年于上海交通大学获得控制理论与控制工程博士学位. 主要研究方向为工业过程的建模, 控制与优化. E-mail: yizheng@sjtu.edu.cn

计量

出版历程

Networked Cooperative Distributed Model Predictive Control for Dynamic Coupling Systems

计量

出版历程

目录

作者简介:
郑毅上海交通大学电子信息与电气工程学院助理研究员. 2010 年于上海交通大学获得控制理论与控制工程博士学位. 主要研究方向为工业过程的建模, 控制与优化. E-mail: yizheng@sjtu.edu.cn