ESO-Based Data Driven Set-Point Learning Control for Nonlinear batch Processes with PD-Type Feedback Control Structure Subject to Nonrepetitive Uncertainties

In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentic...

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Vydáno v:Data Driven Control and Learning Systems Conference (Online) s. 2082 - 2088
Hlavní autoři: Ahmad, Naseem, Hao, Shoulin, Liu, Tao, Gong, Yihui, Zhang, Jiyan, Wang, Haixia, Zhu, Yong
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 17.05.2024
Témata:
Batch production systems
Data-driven set-point learning control
Heuristic algorithms
Iterative algorithms
iterative extended state observer
Learning systems
nonlinear batch processes
nonrepetitive uncertainties
Observers
PD-type feedback control
Process control
Uncertainty
ISSN:2767-9861
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Shrnutí:In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentical initial resetting conditions, by only utilizing the available process input/output data. The controlled process is firstly reformulated as an iterative dynamic linearization data model (IDLDM) with an extra generalized disturbance, where the unknown pseudo partial derivative and the generalized disturbance are, respectively, estimated by a projection-like algorithm and iterative ESO along the batch direction. By virtue of the estimated PPD and generalized disturbance, a set-point learning updating law with adaptive learning gain is then developed to optimize the tracking performance from batch-to-batch, by tuning the set-point command of the closed-loop PD-type control. Robust convergence analysis for the established closed-loop learning system is analyzed rigorously. The effectiveness and superiority of the proposed scheme are verified by an illustrative example.
ISSN:2767-9861
DOI:10.1109/DDCLS61622.2024.10606659