Data-driven set-point learning control with ESO and RBFNN for nonlinear batch processes subject to nonrepetitive uncertainties

This paper proposes an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme for a class of nonlinear batch processes with a priori P-type feedback control structure subject to nonrepetitive uncertainties, by only using the process input and output data available...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	ISA transactions Ročník 146; s. 308 - 318
Hlavní autoři:	Ahmad, Naseem, Hao, Shoulin, Liu, Tao, Gong, Yihui, Wang, Qing-Guo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.03.2024
Témata:	Dynamic linearization data model Extended state observer Nonlinear batch processes Nonrepetitive uncertainties Radial basis function neural network Set-point learning control Nonrepetitive uncertainties Dynamic linearization data model Extended state observer Set-point learning control Nonlinear batch processes Radial basis function neural network
ISSN:	0019-0578, 1879-2022, 1879-2022
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	This paper proposes an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme for a class of nonlinear batch processes with a priori P-type feedback control structure subject to nonrepetitive uncertainties, by only using the process input and output data available in practice. Firstly, the unknown process dynamics is equivalently transformed into an iterative dynamic linearization data model (IDLDM) with a residual term. A radial basis function neural network is adopted to estimate the pseudo partial derivative information related to IDLDM, and meanwhile, a data-driven iterative ESO is constructed to estimate the unknown residual term along the batch direction. Then, an adaptive set-point learning control law is designed to merely regulate the set-point command of the closed-loop control structure for realizing batch optimization. Robust convergence of the output tracking error along the batch direction is rigorously analyzed by using the contraction mapping approach and mathematical induction. Finally, two illustrative examples from the literature are used to validate the effectiveness and advantage of the proposed design. •Novel extended state observer (ESO) based data-driven set-point learning control (DDSPLC).•Iterative dynamic linearized data model (IDLDM) for nonlinear batch processes.•Use the radial basis function neural network (RBFNN) to approximate pseudo partial derivative.•Set-point learning law with an adaptive learning gain for a priori P-type feedback structure.•Robust convergence of the resulting ILC system along the batch direction is rigorously analyzed.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0019-0578 1879-2022 1879-2022
DOI:	10.1016/j.isatra.2023.12.044