散逸性を構造的に保証する離散時間ニューラルネットワークによる散逸ダイナミクスの学習
Learning Dissipative Dynamics with Dissipativity-by-Construction Discrete-Time Neural Networks
離散時間の多層パーセプトロンに制約付きパラメータ化を施し、散逸性を構造的に保証しながらロボットのダイナミクスを学習する手法を提案した。
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著者: Tuan Luong, Hyungpil Moon
分類: cs.RO
原文アブストラクト
Dissipativity is a fundamental system-theoretic property closely related to stability, passivity, and input--output stability, and is particularly important in robotics, where learned dynamics models are often embedded within feedback control loops. However, most existing approaches for learning dissipative dynamics are based on continuous-time formulations, which require ODE solvers during training or inference and can therefore be computationally expensive. Moreover, because practical implementations are inherently discrete-time, direct discretization of a continuous-time passive system does not necessarily preserve passivity, motivating the need for explicit discrete-time guarantees. This study proposes a method for learning incrementally dissipative dynamics from input--output time-series data using a deep multilayer perceptron formulated directly in discrete time. Through a constrained parameterization and a dedicated training procedure, the proposed model guarantees incremental dissipativity by construction rather than through regularization. Lyapunov-based analysis establishes the corresponding dissipativity and stability guarantees, while simulations on robotic dynamical systems demonstrate competitive prediction accuracy, computational efficiency, and consistent preservation of incremental dissipativity compared with baseline methods.