Online Tracking with Predictions for Nonlinear Systems with Koopman Linear Embedding
This paper proposes a model-free predictive tracking algorithm for unknown nonlinear systems with Koopman linear embeddings that leverages Willems' fundamental lemma to achieve dynamic regret decaying exponentially with the prediction horizon, effectively bridging the performance gap between nonlinear and lifted linear counterparts.