Unified frameworks for sampled-data extremum seeking control: Global optimisation and multi-unit systems

Sei Zhen Khong, Dragan Nešić, Ying Tan, Chris Manzie

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2720-2733
Number of pages14
JournalAutomatica
Volume49
Issue number9
DOIs
StatePublished - Sep 1 2013

Keywords

  • Extremum seeking
  • Multi-unit systems
  • Nonconvex global optimisation
  • Robustness
  • Sampled-data control

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