Selected work

Representative Papers

Priority Coordination Games: Hodge Decomposition and a Sharp Design Limit

Oct 05, 2026

This study addresses the limitation that potential game approximations in decentralized priority coordination neglect non-conservative incentive components. By leveraging Hodge decomposition to analyze coordination games, this work quantifies the harmonic components unaccounted for by potential functions and evaluates their impact on steady-state probability currents and entropy production rates, integrating graph-theoretic conflict modeling with log-linear learning dynamics. The primary contribution is the first derivation of a closed-form solution for harmonic energy under linear payoffs. Furthermore, it establishes an error lower bound of 1/(d_max+1) for optimal common-objective models—yielding exactly 1/5 for an eight-vehicle intersection—and proves that the approximation error for non-constant rank payoffs in complete graphs is at least 1/N. These findings demonstrate that such deviations are fundamentally irreducible within mechanism design.

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Residual Learning-Based Control of Vehicle Platoons with $\ell_2$ Stability Guarantees via Recurrent Equilibrium Networks

Sep 27, 2026

This study addresses the control challenges of heterogeneous vehicle platoons subject to parametric uncertainties and external disturbances by proposing a robust control framework based on residual learning. The method designs a nominal controller via linear matrix inequalities (LMIs) and introduces a constrained Recurrent Equilibrium Network (REN) satisfying small-gain conditions for residual compensation, combined with a disturbance observer to ensure local closed-loop and string stability. By offline training the REN to augment the nominal control, experimental results demonstrate that the proposed framework significantly reduces spacing and velocity tracking errors, outperforming conventional nominal controllers.

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Recent publications

Latest Papers

Priority Coordination Games: Hodge Decomposition and a Sharp Design Limit

Oct 05, 2026

This study addresses the limitation that potential game approximations in decentralized priority coordination neglect non-conservative incentive components. By leveraging Hodge decomposition to analyze coordination games, this work quantifies the harmonic components unaccounted for by potential functions and evaluates their impact on steady-state probability currents and entropy production rates, integrating graph-theoretic conflict modeling with log-linear learning dynamics. The primary contribution is the first derivation of a closed-form solution for harmonic energy under linear payoffs. Furthermore, it establishes an error lower bound of 1/(d_max+1) for optimal common-objective models—yielding exactly 1/5 for an eight-vehicle intersection—and proves that the approximation error for non-constant rank payoffs in complete graphs is at least 1/N. These findings demonstrate that such deviations are fundamentally irreducible within mechanism design.

0 citationsRead paper

Residual Learning-Based Control of Vehicle Platoons with $\ell_2$ Stability Guarantees via Recurrent Equilibrium Networks

Sep 27, 2026

This study addresses the control challenges of heterogeneous vehicle platoons subject to parametric uncertainties and external disturbances by proposing a robust control framework based on residual learning. The method designs a nominal controller via linear matrix inequalities (LMIs) and introduces a constrained Recurrent Equilibrium Network (REN) satisfying small-gain conditions for residual compensation, combined with a disturbance observer to ensure local closed-loop and string stability. By offline training the REN to augment the nominal control, experimental results demonstrate that the proposed framework significantly reduces spacing and velocity tracking errors, outperforming conventional nominal controllers.

0 citationsRead paper