Stability-Certified Koopman Observer Design for Nonlinear Systems via Generalized Persidskii Dynamics

TL;DR

This paper introduces a stability-certified Koopman observer for nonlinear systems, leveraging generalized Persidskii dynamics to ensure convergence and robustness under model mismatch and noise.

Contribution

It establishes a novel connection between Koopman observer error dynamics and Persidskii systems, enabling LMI-based gain design for stability certification.

Findings

Achieves exponential convergence in the nominal case.

Demonstrates robustness with bounded disturbances.

Reduces steady-state RMSE by up to 42% compared to existing methods.

Abstract

This paper addresses the problem of nonlinear state estimation for dynamical systems whose governing equations are approximated through Koopman operator liftings. While Koopman-based predictors have demonstrated broad approximation capability for nonlinear dynamics, certifying observer convergence under model mismatch and measurement noise has remained a largely open problem. To resolve this, we establish a structural correspondence between the error dynamics of a Koopman latent-space observer and the class of generalized Persidskii systems, which admits diagonal Lyapunov functions and incremental sector characterizations. Exploiting this connection, we design a nonlinear correction term whose gain is computed via a linear matrix inequality (LMI) that simultaneously certifies input-to-state stability (ISS) of the estimation error with respect to both lifting residuals and external disturbances. Exponential convergence in the nominal case and ultimate boundedness under bounded perturbations are established analytically. Numerical validation on the Van~der~Pol oscillator and a nonlinear robotic arm with friction uncertainty demonstrates that the proposed observer substantially outperforms both the Extended Kalman Filter and a linear Koopman observer in terms of estimation accuracy and robustness, achieving up to a 42\% reduction in steady-state RMSE under lifting mismatch.