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Analysis and correction of ill-conditioned model in multivariable model predictive control
Pan H(潘昊); Zou T(邹涛); Yu HB(于海斌); Du DW(杜德伟)
Department信息服务与智能控制技术研究室
Source PublicationInternational Journal of Modelling, Identification and Control
ISSN1746-6172
2016
Volume26Issue:2Pages:130-139
Indexed ByEI
EI Accession number20163502752887
Contribution Rank1
Funding OrganizationNational Nature Science Foundation under Grant No. 61374112, Research Project ‘Standard development and test verification system construction of intelligent manufacturing systems energy efficiency evaluation method’ of Ministry of Industry and Information Technology of PRC, Independent Topics of Key Laboratory of Networked Control Systems, Chinese Academy of Science under Grant No.WLHKZ2014010, and Project supported by the Zhejiang Open Foundation of the Most Important Subjects.
KeywordModel Predictive Control Multivariable Mpc Ill-conditioned Models Singular Value Decomposition Svd Model Identification Model Mismatch Simulation
AbstractIll-conditioned model usually appears in modelling high purity and complicated production processes such as the high-purity distillation column. Model predictive control (MPC) via closed-loop feedback is a class of methods for which a plant model is used to forecast the outputs or states of controlled systems and to then solve a linear or nonlinear programming with multivariable and constraints by so-called receding horizon optimisation. Ill-conditions can cause many negative effects on the implementation of MPC including system instability and controller failure. In this paper, these phenomena such as output static error and stability decreasing of ill-conditioned model caused in MPC are found by simulating simple examples, and the close correlation between the movement direction of the controlled system output and the characteristics of ill-conditioned model is also observed. The geometry tools and singular value decomposition (SVD) in linear algebra are used to analyse the essential cause of ill-conditioned model generation. A new offline quantitative strategy is proposed to improve the ill-conditioned model. Based on modified model of reengineering and implementation of model predictive control, the closed-loop control performance and stability can be significantly enhanced.
Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/19137
Collection信息服务与智能控制技术研究室
Corresponding AuthorZou T(邹涛); Yu HB(于海斌)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Pan H,Zou T,Yu HB,et al. Analysis and correction of ill-conditioned model in multivariable model predictive control[J]. International Journal of Modelling, Identification and Control,2016,26(2):130-139.
APA Pan H,Zou T,Yu HB,&Du DW.(2016).Analysis and correction of ill-conditioned model in multivariable model predictive control.International Journal of Modelling, Identification and Control,26(2),130-139.
MLA Pan H,et al."Analysis and correction of ill-conditioned model in multivariable model predictive control".International Journal of Modelling, Identification and Control 26.2(2016):130-139.
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