This paper presents a novel approach to improve the global performance of extremum seeking control (ESC). This algorithm employs perturbation-based ESC to find local extrema and a simplex-based algorithm to search for the global extremum. The two methods are employed in an iterative switching manner. We propose a novel frequency-domain criterion to detect when the state of a system under ESC has converged to a local optimum. Switching from ESC to the simplex method is triggered when this criterion is satisfied. We prove that this criterion will be met within finite time, under conditions on the ESC law. Simulations and experiments demonstrate and statistically prove that this algorithm outperforms ESC or simplex-based algorithms for optimizing objective functions with multiple extrema.
['Yinghua Zhang', 'Mario A. Rotea', 'Nicholas R. Gans']
Simplex Guided Extremum Seeking Control With Convergence Detection to Improve Global Performance