Extremum Seeking Control with an Adaptive Gain Based On Gradient Estimation Error

Abstract

This paper presents an extremum-seeking control (esc) algorithm with an adaptive step-size that adjusts the aggressiveness of the controller based on the quality of the gradient estimate. The adaptive step-size ensures that the integral-action produced by the gradient descent does not destabilize the closedloop system. To quantify the quality of the gradient estimate, we present a batch least-squares (bls) estimator with a novel weighting and show that it produces bounded estimation errors, where the uncertainty is due to the curvature of the unknown cost function. The adaptive step-size then maximizes the decrease of the combined plant and controller Lyapunov function for the worst-case estimation error. We prove that our esc controller is input-to-state stable with respect to the dither signal. Finally, we demonstrate our esc controller through five numerical examples; one illustrative, one practical, and three benchmarks.