Performance assessment of decentralized control systems: An iterative approach

被引:9
作者
Liu, Su [1 ]
Liu, Jinfeng [2 ]
Feng, Yiping [1 ]
Rong, Gang [1 ]
机构
[1] Zhejiang Univ, Inst Cyber Syst & Control, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
[2] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada
关键词
Control performance assessment; Decentralized control systems; Optimization-based approach; Block-diagonal structural constraint; Linear discrete time systems; MODEL-PREDICTIVE CONTROL; MINIMUM-VARIANCE BENCHMARK; ECONOMIC-PERFORMANCE; ARCHITECTURES;
D O I
10.1016/j.conengprac.2012.10.003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this work, an efficient approach for performance assessment of decentralized control systems based on a general quadratic performance index involving both system states and inputs is proposed. The performance assessment problem is formulated as an optimization problem subject to constraints in the form of linear/bilinear matrix inequalities which explicitly take the block-diagonal structural constraint on decentralized control systems into account. In order to solve the optimization problem efficiently, an iterative approach based on the original optimization problem and an equivalent transformation of the original one is proposed. Specifically, the proposed approach under the assumption that the full state feedback is available is first presented; and then the approach is extended to the case that only output feedback is available. The proposed approach solves for both the best achievable performance and the corresponding controller (and observer) gains. The application of the proposed approach to two examples including a reactor separator chemical process example illustrates the applicability and effectiveness of the proposed approach. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:252 / 263
页数:12
相关论文
共 39 条
[1]   Assessing model prediction control (MPC) performance. 1. Probabilistic approach for constraint analysis [J].
Agarwal, Nikhil ;
Huang, Biao ;
Tamayo, Edgar C. .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2007, 46 (24) :8101-8111
[2]  
ASTROM K. J., 1970, Introduction to stochastic control
[3]   Decentralized control: An overview [J].
Bakule, Lubomir .
ANNUAL REVIEWS IN CONTROL, 2008, 32 (01) :87-98
[4]   Price-driven coordination method for solving plant-wide MPC problems [J].
Cheng, R. ;
Forbes, J. F. ;
Yip, W. S. .
JOURNAL OF PROCESS CONTROL, 2007, 17 (05) :429-438
[5]  
Christofides P. D., 2011, ADV IND CONTROL
[6]   Distributed model predictive control: A tutorial review and future research directions [J].
Christofides, Panagiotis D. ;
Scattolini, Riccardo ;
Munoz de la Pena, David ;
Liu, Jinfeng .
COMPUTERS & CHEMICAL ENGINEERING, 2013, 51 :21-41
[7]   Extended H2 and H∞ norm characterizations and controller parametrizations for discrete-time systems [J].
De Oliveira, MC ;
Geromel, JC ;
Bernussou, J .
INTERNATIONAL JOURNAL OF CONTROL, 2002, 75 (09) :666-679
[8]   A Lyapunov Function for Economic Optimizing Model Predictive Control [J].
Diehl, Moritz ;
Amrit, Rishi ;
Rawlings, James B. .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2011, 56 (03) :703-707
[9]   A review of performance monitoring and assessment techniques for univariate and multivariate control systems [J].
Harris, TJ ;
Seppala, CT ;
Desborough, LD .
JOURNAL OF PROCESS CONTROL, 1999, 9 (01) :1-17
[10]   Performance assessment of multivariable feedback controllers [J].
Harris, TJ ;
Boudreau, F ;
MacGregor, JF .
AUTOMATICA, 1996, 32 (11) :1505-1518