再帰型ニューラルネットワークモデリングによる安全な学習ベース非線形モデル予測制御に向けて
Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling
非線形モデル予測制御（NMPC）のオンライン計算負荷を軽減するため、予測ホライズン全体でパラメータを共有する逐次ニューラルポリシーを提案し、安全性を保証するオンライン評価とフォールバック機構を組み合わせた。
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: Mihaela-Larisa Clement, Mónika Farsang, Agnes Poks, Johannes Edelmann, Manfred Plöchl, Radu Grosu, Ezio Bartocci
分類: cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
The practical deployment of nonlinear model predictive control (NMPC) is often limited by online computation: solving a nonlinear program at high control rates can be expensive on embedded hardware, especially when models are complex or horizons are long. Learning-based NMPC approximations shift this computation offline but typically demand large expert datasets and costly training. We propose Sequential-AMPC, a sequential neural policy that generates MPC candidate control sequences by sharing parameters across the prediction horizon. For deployment, we wrap the policy in a safety-augmented online evaluation and fallback mechanism, yielding Safe Sequential-AMPC. Compared to a naive feedforward policy baseline across several benchmarks, Sequential-AMPC requires substantially fewer expert MPC rollouts and yields candidate sequences with higher feasibility rates and improved closed-loop safety. On high-dimensional systems, it also exhibits better learning dynamics and performance in fewer epochs while maintaining stable validation improvement where the feedforward baseline can stagnate.
