Gaussian Process based Passivation of a Class of Nonlinear Systems with Unknown Dynamics
For control engineers dealing with nonlinear systems with unknown dynamics, this provides a data-driven method to ensure passivity with probabilistic guarantees, though it is incremental as it applies existing GP techniques to a known control problem.
The paper uses Gaussian process regression to identify unknown dynamics for feed-forward compensation, achieving guaranteed semi-passivity with a specific probability in the closed-loop system. The approach bounds model error using predicted variance and is validated via simulation.
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.