English(EN) A robust and adaptive MPC formulation for Gaussian process models

新的MPC框架使用高斯过程实现鲁棒控制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新颖的模型预测控制（MPC）框架，专为不确定非线性系统设计。该框架利用高斯过程（GPs）从噪声测量中学习系统动力学，并结合从收缩度量中获得的鲁棒预测。所提出的设计通过涉及平面四旋翼飞行器的数值示例证明了递归可行性、可靠的约束满足以及高概率收敛到参考状态。 AI

影响通过改进模型适应不确定性的方式，这项研究可能为机器人和自主应用中的控制系统带来更高的可靠性。

排序理由该集群包含一篇在arXiv上发表的学术论文，详细介绍了一种新的控制方法。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Mathieu Dubied, Amon Lahr, Melanie N. Zeilinger, Johannes K\"ohler · 2026-09-10 04:00

高斯过程模型鲁棒且自适应的MPC方法

arXiv:2507.02098v3 Announce Type: replace-cross Abstract: In this paper, we present a robust and adaptive model predictive control (MPC) framework for uncertain nonlinear systems affected by bounded disturbances and unmodeled nonlinearities. We use Gaussian Processes (GPs) to lea…