Extremum Seeking-Based Indirect Adaptive Control for Nonlinear Systems with State-Dependent Uncertainties

We present in this paper a preliminary result on extremum seeking (ES)-based adaptive trajectory tracking control for nonlinear systems. We propose, for the class of nonlinear systems with parametric uncertainties which can be rendered integral Input-to-State stable (iISS) w.r.t. the parameter estimation errors input, that it is possible to merge together the integral Input-to-State stabilizing feedback controller and a model-free extremum seeking algorithm to realize a learning-based indirect adaptive controller. We show the efficiency of this approach on a mechatronic example.