从轨迹数据中联合学习物理模型与能量调控控制器，实现稳定且可解释的控制。
Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
- 通过交替优化，联合学习物理模型和能量平衡控制器。
- 闭环系统保持被动性，训练中强制能量严格衰减。
- 适合需要稳定性和物理可解释性的机器人控制场景。
我们提出一种基于物理信息的轨迹数据学习框架，用于端口-哈密顿（pH）系统的能量重塑控制。该方法通过交替优化，联合学习一个pH系统模型和最优能量平衡无源性控制（EB-PBC）。每轮迭代中，利用当前控制策略采集的轨迹数据更新系统模型，并在新模型上重新优化控制器。两者均采用嵌入pH动力学和EB-PBC结构的神经网络参数化，保证能量交互的可解释性。所学控制器使闭环系统具有内在无源性并保证稳定性，且不抵消自然势能，而是利用其被动特性。通过耗散正则化，训练中强制严格能量衰减，增强对仿真到现实迁移的鲁棒性。框架在平面和扭转摆系统的状态调节与摆起任务中得到验证。
原文摘要 · Abstract (English)
We develop a physics-informed learning framework for energy-shaping control of port-Hamiltonian (pH) systems from trajectory data. The proposed approach co-learns a pH system model and an optimal energy-balancing passivity-based controller (EB-PBC) through alternating optimization with policy-aware data collection. At each iteration, the system model is refined using trajectory data collected under the current control policy, and the controller is re-optimized on the updated model. Both components are parameterized by neural networks that embed the pH dynamics and EB-PBC structure, ensuring interpretability in terms of energy interactions. The learned controller renders the closed-loop system inherently passive and provably stable, and exploits passive plant dynamics without canceling the natural potential. A dissipation regularization enforces strict energy decay during training, thereby enhancing robustness to sim-to-real gaps. The proposed framework is validated on state-regulation and swing-up tasks for planar and torsional pendulum systems.
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