Learning-based adaptive control for nonlinear systems

We present in this paper a preliminary result on learning-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 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 (ES) algorithm to realize a learning-based adaptive controller. We show the efficiency of this approach on a mechatronic example.