Unified frameworks for sampled-data extremum seeking control: Global optimisation and multi-unit systems
(2013) In Automatica- Abstract
- Two frameworks are proposed for extremum seeking of general nonlinear plants based on a sampled-data control law, within which a broad class of nonlinear programming methods is accommodated. It is established that under some generic assumptions, semi-global practical convergence to a global extremum can be achieved. In the case where the extremum seeking algorithm satisfies a stronger asymptotic stability property, the converging sequence is also shown to be stable using a trajectory-based proof, as opposed to a Lyapunov-function-type approach. The former is more straightforward and insightful. This allows for more general optimisation algorithms than considered in existing literature, such as those which do not admit a state-update... (More)
- Two frameworks are proposed for extremum seeking of general nonlinear plants based on a sampled-data control law, within which a broad class of nonlinear programming methods is accommodated. It is established that under some generic assumptions, semi-global practical convergence to a global extremum can be achieved. In the case where the extremum seeking algorithm satisfies a stronger asymptotic stability property, the converging sequence is also shown to be stable using a trajectory-based proof, as opposed to a Lyapunov-function-type approach. The former is more straightforward and insightful. This allows for more general optimisation algorithms than considered in existing literature, such as those which do not admit a state-update realisation and/or Lyapunov functions. Lying at the heart of the analysis throughout is robustness of the optimisation algorithms to additive perturbations of the objective function. Multi-unit extremum seeking is also investigated with the objective of accelerating the speed of convergence. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4246747
- author
- Khong, Sei Zhen LU ; Nešić, Dragan ; Tan, Ying and Manzie, Chris
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Automatica
- publisher
- Pergamon Press Ltd.
- external identifiers
-
- scopus:84881479515
- ISSN
- 0005-1098
- language
- English
- LU publication?
- no
- id
- ddff399a-230b-4992-a281-dddcd30f663c (old id 4246747)
- date added to LUP
- 2016-04-04 14:30:38
- date last changed
- 2022-03-31 22:36:50
@article{ddff399a-230b-4992-a281-dddcd30f663c,
abstract = {{Two frameworks are proposed for extremum seeking of general nonlinear plants based on a sampled-data control law, within which a broad class of nonlinear programming methods is accommodated. It is established that under some generic assumptions, semi-global practical convergence to a global extremum can be achieved. In the case where the extremum seeking algorithm satisfies a stronger asymptotic stability property, the converging sequence is also shown to be stable using a trajectory-based proof, as opposed to a Lyapunov-function-type approach. The former is more straightforward and insightful. This allows for more general optimisation algorithms than considered in existing literature, such as those which do not admit a state-update realisation and/or Lyapunov functions. Lying at the heart of the analysis throughout is robustness of the optimisation algorithms to additive perturbations of the objective function. Multi-unit extremum seeking is also investigated with the objective of accelerating the speed of convergence.}},
author = {{Khong, Sei Zhen and Nešić, Dragan and Tan, Ying and Manzie, Chris}},
issn = {{0005-1098}},
language = {{eng}},
publisher = {{Pergamon Press Ltd.}},
series = {{Automatica}},
title = {{Unified frameworks for sampled-data extremum seeking control: Global optimisation and multi-unit systems}},
year = {{2013}},
}