定数パラメータを超えて：ハイパー予測モデルとHyperMPC
Beyond Constant Parameters: Hyper Prediction Models and HyperMPC
未モデル化ダイナミクスを時間依存パラメータで表現するニューラルネットベースの予測モデルHyperPMを提案し、MPCに統合したHyperMPCがF1TENTH自律走行などで予測精度と制御性能を向上させることを示した。
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著者: Jan Węgrzynowski, Piotr Kicki, Grzegorz Czechmanowski, Maciej Krupka, Krzysztof Walas
分類: cs.RO
原文アブストラクト
Model Predictive Control (MPC) is among the most widely adopted and reliable methods for robot control, relying critically on an accurate dynamics model. However, existing dynamics models used in the gradient-based MPC are limited by computational complexity and state representation. To address this limitation, we propose the Hyper Prediction Model (HyperPM) - a novel approach in which we project the unmodeled dynamics onto a time-dependent dynamics model. This time-dependency is captured through time-varying model parameters, whose evolution over the MPC prediction horizon is learned using a neural network. Such formulation preserves the computational efficiency and robustness of the base model while equipping it with the capacity to anticipate previously unmodeled phenomena. We evaluated the proposed approach on several challenging systems, including real-world F1TENTH autonomous racing, and demonstrated that it significantly reduces long-horizon prediction errors. Moreover, when integrated within the MPC framework (HyperMPC), our method consistently outperforms existing state-of-the-art techniques.
