Abstract:

In recent years, a number of extremum seeking algorithms have been proposed. While each approach aims to estimate the gradient of a performance metric in realtime and steer inputs to values that optimize the metric, the way in which each method accomplishes this goal can have practical implications that depend on the application. In this paper, we compare the performance of traditional perturbation-based extremum seeking to time-varying extremum seeking in the context of optimizing the energy efficiency of a vapor compression system. In order to benchmark these algorithms, we simulate their performance using a moving-boundary model of a vapor compression machine that has been tuned and calibrated to data gathered from a multi-split style room air conditioner operating in cooling mode. We show that while perturbation-based extremum seeking appears simplest to tune, some challenging minima are not obtained. Also, we find that time-varying extremum seeking converges faster and more reliably than the other method tested.