再帰平衡ネットワークによる残差学習ベースの車両プラトーン制御：ℓ2安定性保証付き
Residual Learning-Based Control of Vehicle Platoons with $\ell_2$ Stability Guarantees via Recurrent Equilibrium Networks
パラメータ不確かさや外乱を受ける異種車両プラトーンに対し、LMIベースの公称制御器と再帰平衡ネットワーク（REN）による残差学習を組み合わせ、ℓ2ゲイン制約を満たすことで安定性を保証する制御フレームワークを提案した。
著者: Brian Delgado, Anh-Tu Nguyen, Hamid Taghavifar
分類: cs.RO, eess.SY
原文アブストラクト
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.