Journal article

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

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



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..

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Awarded by Australian Research Council

Funding Acknowledgements

This work was supported by the Australian Research Council (DP0985388). The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Nathan Van De Wouw under the direction of Editor Andrew R. Teel.