外观
论文arXiv 2509.0956321 天前
面向自由漂浮空间机械臂的基于学习的数据辅助 port-Hamiltonian 控制
原文链接：Learning-Based Data-Assisted Port-Hamiltonian Control for Free-Floating Space Manipulators（arXiv 2509.09563 · 发布日期：2026-10-07）
Learning-Based Data-Assisted Port-Hamiltonian Control for Free-Floating Space Manipulators
提出port-Hamiltonian框架下的数据辅助控制，用学习补偿未建模效应，并在自由漂浮空间机械臂姿态控制案例中验证鲁棒跟踪。
核心要点
- 动机是处理复杂未建模效应，同时避免学习适应中的延迟和瞬态误差破坏主动力学稳定性。
- 方法引入有物理意义的可观测量，并结合鲁棒模型控制器与高增益分散积分器。
- 学习被限制在最简单的动力学部分，Lyapunov分析给出有界学习误差下的收敛保证。
- 案例为含零空间避碰的自由漂浮空间机械臂姿态控制，展示鲁棒跟踪性能和适用性。
验证范围与局限：验证范围为自由漂浮空间机械臂姿态控制案例；摘要未说明仿真或真机验证。
英文摘要 (Abstract)
A generic data-assisted control architecture within the port-Hamiltonian framework is proposed, introducing a physically meaningful observable that links conservative dynamics to all actuation, dissipation, and disturbance channels. A robust, model-based controller combined with a high-gain decentralized integrator establishes large robustness margins and strict time-scale separation, ensuring that subsequent learning cannot destabilize the primary dynamics. Learning, selected for its generalizability, is then applied to capture complex, unmodeled effects, despite inherent delay and transient error during adaptation. Formal Lyapunov analysis with explicit stability bounds guarantees convergence under bounded learning errors. The structured design confines learning to the simplest part of the dynamics, enhancing data efficiency while preserving physical interpretability. The approach is generic, with a free-floating space manipulator orientation control task, including integrated null-space collision avoidance, serving as a case study to demonstrate robust tracking performance and applicability to broader robotic domains.
作者：Mostafa Eslami, Maryam Babazadeh
来源
本页事实对应的公开页面，以原文为准。