強化学習によるクアッドローターの適応型アウターループ制御
Adaptive Outer-Loop Control of Quadrotors via Reinforcement Learning
クアッドローターの飛行制御に強化学習を適用し、外乱を推定する残差ダイナミクス予測器と実機転送のための校正機構を導入して、動的擾乱下でも高精度な軌道追従を実現した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Vishnu Saj, Sushil Vemuri, Dileep Kalathil, Moble Benedict
分類: cs.RO, cs.LG
原文アブストラクト
Deep Reinforcement Learning (DRL) for quadrotor flight control typically relies on Domain Randomization (DR) for sim-to-real transfer, resulting in overly conservative policies that struggle with dynamic disturbances. To overcome this, we propose a novel adaptive control architecture that actively perceives and reacts to instantaneous perturbations. First, we train an optimal outer-loop policy, then replace its reliance on ground-truth disturbance data with a Residual Dynamics Predictor (RDP). The RDP estimates the external forces and moments acting on the aircraft in flight online using only the history of states and control actions. For seamless hardware transfer, we introduce a data-efficient linear calibration bridge and an online thrust correction mechanism that align the simulated latent space with reality using mere seconds of flight data. Real-world validations on a Crazyflie micro-quadrotor demonstrate that our adaptive controller significantly outperforms baselines, maintaining precise trajectory tracking under severe uncertainties including mass variations, asymmetric payloads, and dynamic slung loads
