The problem of steering the state of nonlinearly perturbed linear syst
ems by learning is investigated. A family of algorithms which compute
the steering control by means of successive trials on the real plant i
s presented. Convergence in the face of a class of nonlinear plant per
turbations is proven. State feedback linearizable systems are shown to
be addressable by the presented algorithms. Two examples illustrate t
he applicability of algorithms.