Learning control for nonlinear systems in output feedback form

The class of single-input, single-output, minimum phase, nonlinear, time-invariant systems with unknown output-dependent nonlinearities, unknown parameters and known relative degree ρ is considered. The output regulation problem is addressed and solved in the presence of unknown periodic reference and/or disturbance signals of known common period. A simple learning control algorithm is designed which guarantees asymptotic output tracking for any initial condition belonging to any given connected compact set. It can be interpreted as a generalization of the classical View the MathML source control which solves the regulator problem when reference and disturbance signals are constant. As far as linear systems are concerned, global results are achieved.