Y. de Santis, Michele Pierallini, Franco Angelini, Manolo Garabini · 2026 · Control Engineering Practice
DOI: 10.1016/j.conengprac.2026.107166 · cited by 0 works (OpenAlex) · open-access PDF
合规机器人的轨道跟踪是具有挑战性的,因为非线性动态和建模不确定性,往往需要基于学习的方法来避免明确的参数识别。 迭代学习控制(ILC)确保了理论上的高精度,并保持了机器人的被动合规性。 然而,传统的ILC需要每个新轨道的单独学习阶段。 相反,增强学习(RL)将普遍化到新的轨道,但实际上受到了高样本复杂性和有限的跟踪精度。
Machine translation (Meta M2M100); the English original follows.
Trajectory tracking for compliant robots is challenging due to nonlinear dynamics and modeling uncertainties, often requiring learning-based methods to avoid explicit parameter identification. Iterative Learning Control (ILC) ensures theoretically high precision and preserves the passive compliance of the robot. However, traditional ILC requires a separate learning phase for each new trajectory. Conversely, Reinforcement Learning (RL) generalizes to new trajectories, but practically suffers from high sample complexity and limited tracking precision. This work proposes Reinforced Iterative Lear
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Topics: Iterative learning control, Generalization, Control theory (sociology), Joint (building), Computer science, Control (management)
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