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题名: Analysis and correction of ill-conditioned model in multivariable model predictive control
作者: Pan H(潘昊); Zou T(邹涛); Yu HB(于海斌); Du DW(杜德伟)
作者部门: 信息服务与智能控制技术研究室
通讯作者: 邹涛
关键词: model predictive control ; multivariable MPC ; ill-conditioned models ; singular value decomposition ; SVD ; model identification ; model mismatch ; simulation
刊名: International Journal of Modelling, Identification and Control
ISSN号: 1746-6172
出版日期: 2016
卷号: 26, 期号:2, 页码:130-139
收录类别: EI
产权排序: 1
摘要: Ill-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.
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/19137
Appears in Collections:信息服务与智能控制技术研究室_期刊论文

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Recommended Citation:
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.
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