Control-aware Learning of Koopman Embedding Models

TL;DR

This paper introduces a control-aware learning method for Koopman embedding models that enhances closed-loop control performance by refining neural network-based observables and employing a strategic data sampling approach.

Contribution

It presents a novel technique to improve Koopman models for control by refining learned embeddings and optimizing data sampling, addressing issues in closed-loop deployment.

Findings

Enhanced control performance in nonlinear systems

Refined models maintain state prediction accuracy

Data sampling strategy improves controller robustness

Abstract

A learning method is proposed for Koopman operator-based models with the goal of improving closed-loop control behavior. A neural network-based approach is used to discover a space of observables in which nonlinear dynamics is linearly embedded. While accurate state predictions can be expected with the use of such complex state-to-observable maps, undesirable side-effects may be introduced when the model is deployed in a closed-loop environment. This is because of modeling or residual error in the linear embedding process, which can manifest itself in a different manner compared to the state prediction. To this end, a technique is proposed to refine the originally trained model with the goal of improving the closed-loop behavior of the model while retaining the state-prediction accuracy obtained in the initial learning. Finally, a simple data sampling strategy is proposed to use inputs deterministically sampled from continuous functions, leading to additional improvements in the controller performance for nonlinear dynamical systems. Several numerical examples are provided to show the efficacy of the proposed method.