Port Hamiltonian systems have grown in interest in recent years due to their modular property, close relation with physical modelling and the interesting properties arising from that. In this paper, we aim at providing an overview of the application of machine learning for port Hamiltonian systems in terms of modelling and control.

After an introduction to Port Hamiltonian systems framework, recent results on Hamiltonian systems modelling are presented. Some results on minimal realization and model reduction are then overviewed. Finally, the most important results on the control of Port Hamiltonian systems based machine learning are discussed including adaptive control, iterative control and reinforcement learning. The results presented in this paper are a motivation for the potential of applying machine learning methods to dynamical systems in general and port Hamiltonian systems in particular.

近年来，由于其模块特性，与物理模型的密切关系以及由此产生的有趣特性，哈密尔顿港系统引起了人们的关注。在本文中，我们旨在从建模和控制方面概述哈密顿港口系统机器学习的应用。

在介绍了哈密顿港口系统框架之后，介绍了有关哈密顿系统建模的最新结果。然后概述了有关最小实现和模型简化的一些结果。最后，讨论了基于哈密顿系统的机器学习控制的最重要成果，包括自适应控制，迭代控制和强化学习。本文中提出的结果激发了将机器学习方法应用于一般动力系统，尤其是港口哈密顿系统的潜力。