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Author

H. Modares

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Open access 2026

Error Bounds for Finite-Dimensional Stochastic Koopman Approximations

Koopman operator theory provides a popular framework for lifting a nonlinear dynamical system to a linear system in a higher dimensional space using a dictionary of observable functions. When a finite-dimensional dictionary is selected, it is important to determine whether the corresponding observable subspace is invar...

S. R. Jafari, H. Modares, F. Yaghmaie · 0 citations
Open access 2026

SAFE–MA–RRT: Data-Driven Safe Motion Planning for Multi-Agent Systems

This paper proposes a fully data-driven motion-planning framework for homogeneous linear multi-agent systems that operate in shared, obstacle-filled workspaces without access to explicit system models. Each agent independently learns its closed-loop behavior from experimental data by solving convex semidefinite program...

Babak Esmaeili, H. Modares · 0 citations
Preprint Jul 2026

Direct Data Driven Natural Gradient Descent for Control

This paper proposes two data-driven NGD formulations that incorporate the closed-loop covariance matrix through the Fisher Information Matrix (FIM), allowing gradient updates to be preconditioned according to the system's intrinsic uncertainty.

Ramin Esmzad, F. Yaghmaie, Bahare Kiumarsi et al. · 0 citations

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