Fast extremum seeking on Hammerstein plants: A model-based approach

Sharafi, Jalil; Moase, William H.; Manzie, Chris

doi:10.1016/j.automatica.2015.06.024

Cited by 11 publications

(1 citation statement)

References 24 publications

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“…The latter also presents an experimental validation of the method on a spark-ignition engine. Eventually, the same authors proposed directly adding some model knowledge in the adaptation scheme of the fast extremum seeking method in [91]. Interestingly, this allows improving convergence not by completely removing the time-scale separations of, in decreasing speed order, the optimizer and the estimator, but by reversing them, that is, by accelerating the estimation and subsequently the optimization.…”

Section: Dynamic Modeling With Block-oriented Representationsmentioning

confidence: 99%

Model-Free Extremum Seeking Control of Bioprocesses: A Review with a Worked Example

Dewasme

Wouwer

2020

Processes

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Uncertainty is a common feature of biological systems, and model-free extremum-seeking control has proved a relevant approach to avoid the typical problems related to model-based optimization, e.g., time- and resource-consuming derivation and identification of dynamic models, and lack of robustness of optimal control. In this article, a review of the past and current trends in model-free extremum seeking is proposed with an emphasis on finding optimal operating conditions of bioprocesses. This review is illustrated with a simple simulation case study which allows a comparative evaluation of a few selected methods. Finally, some experimental case studies are discussed. As usual, practice lags behind theory, but recent developments confirm the applicability of the approach at the laboratory scale and are encouraging a transfer to industrial scale.

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Section: Dynamic Modeling With Block-oriented Representationsmentioning

confidence: 99%

Model-Free Extremum Seeking Control of Bioprocesses: A Review with a Worked Example

Dewasme

Wouwer

2020

Processes

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Model‐guided extremum seeking–case studies

Tan

Chen

Hua

et al. 2021

Adaptive Control & Signal

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In practice, data-driven control and optimization techniques are applied to address problems in engineering systems of which the model is either unavailable or so complicated that a model-based analytic design can be hardly carried. Among them, the extremum seeking (ES) is a popular model-free or data-driven optimization method that has been effectively applied to provide optimal solutions to various industrial control systems. In this article, a new design philosophy, called the model-guided ES, which is a special case of model-guided data-driven (MGDD) optimization, is presented and demonstrated with two successful case studies. In particular, it is shown that, in these two cases, how models of physical systems, even if imperfect or developed in a data-driven way instead of the first-principle based approach, could be integrated together with the conventional ES algorithm to deliver much improved and guaranteed convergence performance and the ultimate bound. It is noted that the first case is for the automotive diesel engine optimization and the second case for the automated regulation of LiDAR detection range. Both cases are successfully validated with experiments.

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