Modelling and optimal state-delay control in microbial batch process

In this paper, we consider optimal control problem involving a time-varying state-delay system arising in 1,3-propanediol microbial batch process. The dynamic system in this problem includes unknown time-varying delay function and unknown kinetic parameters. To optimally determine the unknown delay...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Applied Mathematical Modelling Ročník 89; s. 792
Hlavní autoři: Liu, Chongyang, Gong, Zhaohua, Teo, Kok Lay, Wang, Song
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier BV 01.01.2021
Témata:
Basis functions
Delay
Evolutionary algorithms
Evolutionary computation
Microorganisms
Optimal control
Optimization
Parameters
ISSN:1088-8691, 0307-904X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, we consider optimal control problem involving a time-varying state-delay system arising in 1,3-propanediol microbial batch process. The dynamic system in this problem includes unknown time-varying delay function and unknown kinetic parameters. To optimally determine the unknown delay function and unknown kinetic parameters in the system, the weighted least-squares error between the computed values and experimental data is minimized subject to path constraints. By parameterizing the delay function with piecewise quadratic basis functions, the optimal state-delay control problem is approximated by a sequence of parameter optimization problems. Furthermore, an exact penalty method is utilized to transform these parameter optimization problems into the ones only with box constraints. On this basis, a modified differential evolution algorithm is developed to solve the resulting optimization problems. Finally, numerical results are presented to verify the effectiveness of the developed solution approach.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1088-8691
0307-904X
DOI:10.1016/j.apm.2020.07.051