LAPSE:2020.0360
Published Article
LAPSE:2020.0360
A Control-Performance-Based Partitioning Operating Space Approach in a Heterogeneous Multiple Model
Bing Wu, Ximei Liu, Yaobin Yue
April 14, 2020
An operating space partition method with control performance is proposed, where the heterogeneous multiple model is applied to a nonlinear system. Firstly, the heterogeneous multiple model is obtained from a nonlinear system at the given equilibrium points and transformed into a homogeneous multiple model with auxiliary variables. Secondly, an optimal problem where decision variables are composed of control input and boundary conditions of sub-models is formulated with the hybrid model developed from the homogeneous multiple model. The computational implementation of an optimal operating space partition algorithm is presented according to the Hamilton−Jacobi−Bellman equation and numerical method. Finally, a multiple model predictive controller is designed, and the computational implementation of the multiple model predictive controller is addressed with the auxiliary vectors. Furthermore, a continuous stirred tank reactor (CSTR) is used to confirm the effectiveness of the developed method as well as compare with other operating space decomposition methods.
Keywords
heterogeneous multiple model, Model Predictive Control, nonlinear system, operating space partition
Suggested Citation
Wu B, Liu X, Yue Y. A Control-Performance-Based Partitioning Operating Space Approach in a Heterogeneous Multiple Model. (2020). LAPSE:2020.0360
Author Affiliations
Wu B: College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao 266061, China [ORCID]
Liu X: College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao 266061, China
Yue Y: College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao 266061, China
Journal Name
Processes
Volume
8
Issue
2
Article Number
E215
Year
2020
Publication Date
2020-02-11
Published Version
ISSN
2227-9717
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PII: pr8020215, Publication Type: Journal Article
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LAPSE:2020.0360
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doi:10.3390/pr8020215
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Apr 14, 2020
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Apr 14, 2020
 
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Calvin Tsay
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