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    Robust multi-objective optimal switching control arising in 1,3-propanediol microbial fed-batch process

    257093.pdf (315.1Kb)
    Access Status
    Open access
    Authors
    Liu, Chongyang
    Gong, Z.
    Teo, Kok Lay
    Sun, Jie
    Caccetta, Louis
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Liu, C. and Gong, Z. and Teo, K.L. and Sun, J. and Caccetta, L. 2017. Robust multi-objective optimal switching control arising in 1,3-propanediol microbial fed-batch process. Nonlinear Analysis: Hybrid Systems. 25: pp. 1-20.
    Source Title
    Nonlinear Analysis: Hybrid Systems
    DOI
    10.1016/j.nahs.2017.01.006
    ISSN
    1751-570X
    School
    Department of Mathematics and Statistics
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160102819
    http://purl.org/au-research/grants/arc/DP140100289
    URI
    http://hdl.handle.net/20.500.11937/58238
    Collection
    • Curtin Research Publications
    Abstract

    This paper considers optimal control of glycerol producing 1,3-propanediol (1,3-PD) via microbial fed-batch fermentation. The fed-batch process is formulated as a nonlinear switched time-delay system. In general, the time-delay in the fed-batch process cannot be exactly estimated. Our goal is to design an optimal switching control scheme to simultaneously maximize 1,3-PD productivity and 1,3-PD yield under time-delay uncertainty. Accordingly, we propose a robust multi-objective optimal switching control model, in which two objectives, i.e., 1,3-PD productivity and 1,3-PD yield, and their sensitivities with respect to uncertain time-delay are considered in the objective vector. The control variables in this problem are the feeding rate of glycerol, the switching instants and the terminal time of the process. By introducing an auxiliary dynamic system to calculate the objective sensitivities and performing a time-scaling transformation, we obtain an equivalent multi-objective optimal switching control problem in standard form. We then convert the equivalent multi-objective optimal control problem into a sequence of single-objective optimal switching control problems by using a modified normal boundary intersection method. A novel gradient-based single-objective solver combining control parameterization with constraint transcription technique is developed to solve these resulting single-objective optimal control problems. Finally, numerical results are provided to verify the effectiveness of the proposed solution approach.

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