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

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• 作者：Sei Zhen Khong ; Dragan Ne?i? ; Ying Tan ; Chris Manzie
• 关键词：Extremum seeking ; Sampled-data control ; Nonconvex global optimisation ; Robustness ; Multi-unit systems
• 刊名：Automatica
• 出版年：2013
• 出版时间：September, 2013
• 年：2013
• 卷：49
• 期：9
• 页码：2720-2733
• 全文大小：899 K

文摘

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.