四重項制御における過渡的な分布外外乱への適応学習のための双勾配合意
Dual Gradient Consensus for Adaptive Learning to Transient Out-of-Distribution Disturbances in Quadrotor Control
四重項の軌道追従において、短期的な分布外残差が学習済み外乱モデルを壊す問題に対し、残差勾配を長期・短期記憶と比較して適応状態を分類し、信頼度マップで更新を抑制するDual Gradient Consensusを提案。4つの混合外乱ケースで平均位置RMSEが最小。
著者: Mingtian Tan, Vasileios Tzoumas
分類: cs.RO, eess.SY
原文アブストラクト
Quadrotor trajectory tracking under unmodeled disturbances often relies on online residual adaptation, but short out-domain residuals can corrupt the learned disturbance model when constant-step updates treat every residual gradient uniformly. We propose Dual Gradient Consensus (DGC), an adaptive-learning method for mixed in-domain/out-domain disturbances that retains the random Fourier feature model, offline BPTT policy, and least-squares adaptation of a prior domain-adaptive residual policy (DAPL) while modifying the online update step size. DGC compares each residual gradient with long- and short-term gradient memories, classifies the current adaptation regime, and uses a confidence map to suppress unreliable updates while recovering adaptation after persistent domain shifts. We evaluate DGC in numerical quadrotor trajectory tracking under four residuals that mix an in-domain component with short out-domain events. Compared with $L_1$-augmented MPC, RFF-based MPC, constant-step DAPL, and a frozen-parameter baseline, DGC achieves the lowest mean position RMSE in all four cases.