新方法使用反向损失来稳定反馈控制器

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种设计反馈控制器的方法，该方法可以通过学习其潜在动力学来证明稳定复杂系统。该方法使用“反向损失”来鼓励潜在轨迹重建完整的状态轨迹，特别是对于线性时不变系统。研究表明，足够长的重建范围和足够的潜在维度对于确保使用这些学习模型设计的控制器能够有效地稳定原始系统至关重要，即使在非线性情况下也是如此。 AI

影响这项研究可能导致机器人技术和其他复杂动态环境中更鲁棒且可证明稳定的控制系统。

排序理由该集群是发表在arXiv上的研究论文，详细介绍了一种设计反馈控制器的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Paul Lutkus, Lars Lindemann, Stephen Tu · 2026-11-02 05:00

Learning Stabilizing Latent Feedback Controllers via Multi-Step Backward Losses

arXiv:2610.40931v1 Announce Type: cross Abstract: Can learned latent dynamics models support the design of feedback controllers that provably stabilize the original system? For linear time-invariant systems, we show that learning linear autoencoders and latent dynamics with multi…

本文如何被排名

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Learning Stabilizing Latent Feedback Controllers via Multi-Step Backward Losses

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