用能量函数学习提升机器人轨迹跟踪的精度与稳定性
L-Learning : A Lyapunov-Based Approach Leveraging Lagrangian Mechanics for Efficient and Stable Robot Tracking
- 结合李雅普诺夫理论与拉格朗日力学，从数据中学习系统能量函数
- 在动态环境中实现高精度跟踪，且具备理论稳定保证
- 样本效率高，适合真实机器人部署
本文提出L-Learning，一种融合李雅普诺夫稳定性理论与拉格朗日力学的数据驱动控制框架，用于提升机器人轨迹跟踪性能。传统控制方法在动态不确定环境下易性能退化，而数据驱动方法常因样本复杂度高且缺乏严格的稳定性保障受限。L-Learning通过从数据中显式学习系统的能量函数，在优化性能的同时内生地确保闭环稳定性。该方法具有更高的控制精度、理论稳定性保证和高样本效率，为实际机器人应用提供了有前景的解决方案。
原文摘要 · Abstract (English)
This paper presents L-Learning, a novel data-driven control framework for robotics that integrates Lyapunov stability theory with Lagrangian mechanics to enhance trajectory tracking performance. While traditional control methods often suffer from performance degradation in dynamic and uncertain environments, data-driven approaches, while more adaptable, are frequently limited by high sample complexity and a lack of rigorous stability guarantees. L-Learning mitigates these challenges by explicitly learning the system's energy function from data, thereby optimizing performance while ensuring closed-loop stability intrinsically. Characterized by superior control accuracy, theoretical stability guarantees, and high sample efficiency, L-Learning represents a promising solution for practical robotic applications.
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