Robust suboptimal feedback control for a fed-batch nonlinear time-delayed switched system

被引:4
|
作者
Yuan, Jinlong [1 ,2 ]
Wu, Changzhi [3 ]
Liu, Chongyang [4 ]
Teo, Kok Lay [5 ]
Xie, Jun [6 ]
机构
[1] Dalian Maritime Univ, Sch Sci, Dept Appl Math, Dalian 116026, Peoples R China
[2] Dalian Maritime Univ, Coll Transportat Engn, Dalian 116026, Liaoning, Peoples R China
[3] Guangzhou Univ, Sch Management, Guangzhou 510006, Peoples R China
[4] Shandong Inst Business & Technol, Sch Math & Informat Sci, Yantai 264005, Peoples R China
[5] Sunway Univ, Sch Math Sci, Bandar Sunway 47500, Selangor Darul, Malaysia
[6] PLA Dalian Naval Acad, Dept Basics, Teaching & Res Off Math, Dalian 116018, Liaoning, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金; 澳大利亚研究理事会;
关键词
KLEBSIELLA-PNEUMONIAE; MICROBIAL-PRODUCTION; 1,3-PROPANEDIOL; FERMENTATION; OPTIMIZATION;
D O I
10.1016/j.jfranklin.2022.12.027
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The optimal control strategy constructed in the form of a state feedback is effective for small state perturbations caused by changes in modeling uncertainty. In this paper, we investigate a robust subop-timal feedback control (RSPFC) problem governed by a nonlinear time-delayed switched system with uncertain time delay arising in a 1,3-propanediol (1,3-PD) microbial fed-batch process. The feedback control strategy is designed based on the radial basis function to balance the two (possibly competing) objectives: (i) the system performance (concentration of 1,3-PD at the terminal time of the fermentation) is to be optimal; and (ii) the system sensitivity (the system performance with respect to the uncertainty of the time-delay) is to be minimized. The RSPFC problem is subject to the continuous state inequal-ity constraints. An exact penalty method and a novel time scaling transformation approach are used to transform the RSPFC problem into the one subject only to box constraints. The resulting problem is solved by a hybrid optimization algorithm based on a filled function method and a gradient-based algorithm. Numerical results are given to verify the effectiveness of the developed hybrid optimization algorithm. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:1835 / 1869
页数:35
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