Robust suboptimal feedback control for a fed-batch nonlinear time-delayed switched system
•We propose a robust suboptimal feedback control problem designed based on the radial basis function.•This problem is approximated as a sequence of nonlinear programming subproblems by some techniques.•A hybrid algorithm is proposed to escape local optimal solutions. The optimal control strategy con...
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| Veröffentlicht in: | Journal of the Franklin Institute Jg. 360; H. 3; S. 1835 - 1869 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Inc
01.02.2023
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| ISSN: | 0016-0032, 1879-2693 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | •We propose a robust suboptimal feedback control problem designed based on the radial basis function.•This problem is approximated as a sequence of nonlinear programming subproblems by some techniques.•A hybrid algorithm is proposed to escape local optimal solutions.
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 suboptimal 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 inequality 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. |
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| ISSN: | 0016-0032 1879-2693 |
| DOI: | 10.1016/j.jfranklin.2022.12.027 |