Title: Gaussian Process based Passivation of a Class of Nonlinear Systems with Unknown Dynamics,
Abstract: The paper addresses the problem of passivation of a class of nonlinear
systems where the dynamics are unknown. For this purpose, we use the highly
flexible, data-driven Gaussian process regression for the identification of the
unknown dynamics for feed-forward compensation. The closed loop system of the
nonlinear system, the Gaussian process model and a feedback control law is
guaranteed to be semi-passive with a specific probability. The predicted
variance of the Gaussian process regression is used to bound the model error
which additionally allows to specify the state space region where the
closed-loop system behaves passive. Finally, the theoretical results are
illustrated by a simulation.