On sampled-data extremum seeking control via stochastic approximation methods

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

Research output: Contribution to conferencePaper, not in proceedingpeer-review


This note establishes a link between stochastic approximation and extremum seeking of dynamical nonlinear systems. In particular, it is shown that by applying classes of stochastic approximation methods to dynamical systems via periodic sampled-data control, convergence analysis can be performed using standard tools in stochastic approximation. A tuning parameter within this framework is the period of the synchronised sampler and hold device, which is also the waiting time during which the system dynamics settle to within a controllable neighbourhood of the steady-state input-output behaviour. Semiglobal convergence with probability one is demonstrated for three basic classes of stochastic approximation methods: finite-difference, random directions, and simultaneous perturbation. The tradeoff between the speed of convergence and accuracy is also discussed within the context of asymptotic normality of the outputs of these optimisation algorithms.
Original languageEnglish
Publication statusPublished - 2013
Externally publishedYes
EventAsian Control Conference (ASCC2013) - Istanbul, Turkey
Duration: 2013 Jun 232013 Jun 26


ConferenceAsian Control Conference (ASCC2013)

Subject classification (UKÄ)

  • Control Engineering


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