  Extremum seeking systems are powerful methods able to steer the input of a
(dynamical) cost function towards an optimizer, without any prior knowledge of
the cost function. To achieve their objective, they typically combine
time-periodic signals with the on-line measurement of the cost. However, in
some practical applications, the cost can only be measured during some regular
time-intervals, and not continuously, contravening the classical extremum
seeking framework. In this paper, we first analyze how existing Lie-bracket
based extremum seeking systems behave when being fed with intermittent
measurements, instead of continuous ones. We then propose two modifications of
those schemes to improve both the convergence time and the steady-state
accuracy in presence of intermittent measurements. The performances of the
different schemes are compared on a case study.
