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

Abstract:This paper proposes a residual learning-based control framework for heterogeneous vehicle platoons subject to parametric uncertainty and external disturbances. A nominal controller designed via Linear Matrix Inequalities (LMIs), along with disturbance-observer compensation, is enhanced by a Recurrent Equilibrium Network (REN) trained offline using stored trajectories and nominal-model prediction errors. The REN is constrained to satisfy a prescribed $\ell_2$-gain bound, enabling sufficient small-gain conditions for local closed-loop stability and disturbance string stability. Experiments demonstrate reduced spacing and velocity errors relative to the nominal controller.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2609.33892 [cs.RO]

(or arXiv:2609.33892v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.33892 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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