Efficiency Increase and Start-Up Strategy of an Axial Turbine Stage Under Periodic Inflow Conditions Using Extremum Seeking Control

Topalovic, Daniel; King, Rudibert; Herbig, Markus; Heinrich, Alexander; Peitsch, Dieter

doi:10.1007/978-3-030-90727-3_18

Cited by 1 publication

(1 citation statement)

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“…Extremum seeking control (ESC) is shown to be a convenient approach for solving real-time optimization problems (Hazeleger et al, 2020; Sharma and Alleyne, 2020; Topalovic et al, 2021; Zhao et al, 2020; Ziolkowski et al, 2022). ESC is able to force a dynamical system to converge to an unknown optimal operating point even without knowing the plant model (Duenas et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Perturbation observer design based on extremum seeking control: An analytical insight

Taleshian

Noei

Sadati

et al. 2023

Transactions of the Institute of Measurement and Control

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In this paper, an optimal extended state observer (ESO) is designed for nonlinear integral chain system. For the first time, in this paper, proportional–integral (PI) extremum seeking control (ESC; PIESC) approach is applied to design a perturbation observer by minimizing the estimation error. The PI type structure of the ESC provides fast transient response for the closed-loop system to attain optimum equilibrium point. A mathematical proof is presented to show that the average error dynamic asymptotically converges to zero while the estimation error is minimized. The simulation results verify that the estimation error achieved by the proposed PIESC-based ESO (PIESC-ESO) is found significantly less than that achieved by the classic ESO and PSO-based ESO. Moreover, if slow changes occur within the system or external to it, the PIESC-ESO is still able to modify the observer gains such that the estimation error is minimized again. Therefore, this may help PIESC-ESO to be used in practical applications.

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