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arXiv · 2603.27548

Control Forward-Backward Consistency: Quantifying the Accuracy of Koopman Control Family Models

Masih Haseli·Jorge Cortés·Joel W. Burdick
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Abstract

This paper extends the forward-backward consistency index, originally introduced in Koopman modeling of systems without input, to the setting of control systems, providing a closed-form computable measure of accuracy for data-driven models associated with the Koopman Control Family (KCF). Building on a forward-backward regression perspective, we introduce the control forward-backward consistency matrix and demonstrate that it possesses several favorable properties. Our main result establishes that the relative root-mean-square error of KCF function predictors is strictly bounded by the square root of the control consistency index, defined as the maximum eigenvalue of the consistency matrix. This provides a sharp, closed-form computable error bound for finite-dimensional KCF models. We further specialize this bound to the widely used lifted linear and bilinear models. We also discuss how the control consistency index can be incorporated into optimization-based modeling and illustrate the methodology via simulations.

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Masih Haseli, Jorge Cortés, Joel W. Burdick. 2026-03-29. Control Forward-Backward Consistency: Quantifying the Accuracy of Koopman Control Family Models. https://arxiv.org/abs/2603.27548

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