用虚拟模型设计机器人控制器，自动优化性能并保证稳定。
Optimal Virtual Model Control for Robotics: Design and Tuning of Passivity-Based Controllers
- 通过虚拟机械系统定义控制动作，让机器人行为符合物理规律。
- 利用算法微分对刚体动力学仿真进行优化，实现性能最优。
- 适合需要高稳定性与精确控制的机器人系统设计者。
基于能量的控制是控制理论的核心，也是机器人领域的成熟设计方法。其优势源于无源性定理，为机器人提供强大的互联框架。然而，基于无源性的控制器设计与最优调参仍具挑战。本文提出一种适用于全驱动机器人的直观设计方法，控制动作由经典虚拟模型控制中的‘虚拟机制’决定。所设计的机器人控制行为可从物理角度理解。通过将算法微分应用于刚体动力学的常微分方程仿真，实现最优调参。整体上，该方法兼具灵活性与高效性：虚拟机制的无源性保证系统稳定，而算法微分优化则提升控制性能。
原文摘要 · Abstract (English)
Passivity-based control is a cornerstone of control theory and an established design approach in robotics. Its strength is based on the passivity theorem, which provides a powerful interconnection framework for robotics. However, the design of passivity-based controllers and their optimal tuning remain challenging. We propose here an intuitive design approach for fully actuated robots, where the control action is determined by a `virtual-mechanism' as in classical virtual model control. The result is a robot whose controlled behavior can be understood in terms of physics. We achieve optimal tuning by applying algorithmic differentiation to ODE simulations of the rigid body dynamics. Overall, this leads to a flexible design and optimization approach: stability is proven by passivity of the virtual mechanism, while performance is obtained by optimization using algorithmic differentiation.
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