arXiv cs.AI· Daniele Affinita, Mingda Xu, Beno\^it Valentin Gherardi, Pascal Fua·· 3 天前AI 评分22
基于神经代理模型的气动外形与控制协同设计
Neural-Surrogate Co-Design of Aerodynamic Shape and Control
AI 导读
研究提出一种基于梯度的双层优化框架，用可微神经代理模型替代传统动力学模型，并通过结构利用方法对轨迹优化器求导，实现气动外形与控制器的协同设计。在固定翼滑翔机上以 NeuralFoil 作为气动代理验证，2D 翼型和 3D 机翼实验显示该方法比 SOTA 进化基线快约五倍，并优于仅优化气动代理指标的流程。
正文 · 原文
Abstract:Control co-design jointly optimizes a robot's physical design and its controller. Existing techniques are limited in the number of model parameters and the complexity of the physical models they can handle. In this paper, we propose a gradient-based bilevel formulation that replaces the dynamics models of earlier techniques with a differentiable neural surrogate and differentiates through the trajectory optimizer using a structure-exploiting approach. We validate the framework on fixed-wing gliders, using NeuralFoil as the aerodynamic surrogate. The tasks are maximum-distance gliding, minimum-distance landing, and perching. Experiments on 2D airfoils and 3D wings show that our method runs about five times faster than a state-of-the-art evolutionary baseline. It also outperforms pipelines that optimize aerodynamic proxy metrics.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.06760 [cs.RO] |
| (or arXiv:2603.06760v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2603.06760 arXiv-issued DOI via DataCite |
Submission history
From: Daniele Affinita [view email]
[v1]
Fri, 6 Mar 2026 14:18:18 UTC (3,130 KB)
[v2]
Tue, 13 Oct 2026 11:52:36 UTC (1,447 KB)
来源：arXiv cs.AI · arxiv.org