标题： 基于物理引导的门控循环单元的反演前馈控制 显示英文标题

标题： Physics-guided gated recurrent units for inversion-based feedforward control

摘要： 基于逆的前馈控制依赖于一个准确描述逆系统动态的模型。门控循环单元（GRU），这是循环神经网络中的一种最新架构，是通过数据获得此类模型的有力候选。然而，由于其黑箱特性，GRUs面临可解释性有限和过拟合脆弱等挑战。最近，引入了物理引导神经网络（PGNN），它将先验物理模型结构整合到预测过程中。这种方法不仅提高了训练收敛性，还促进了基于物理的模型学习。在本工作中，我们将GRU集成到PGNN框架中，以获得PG-GRU，并基于此采用两步方法进行前馈控制设计。首先，我们采用稳定逆技术设计逆动态的稳定线性模型。然后，对残差进行训练的GRU被调整用于逆系统识别。所得到的PG-GRU前馈控制器通过在双质量弹簧阻尼系统上的实际生活实验进行了验证，与线性前馈和基于预览的GRU前馈相比，在积分绝对误差方面表现出大约两倍的改进。 显示英文摘要

摘要： Inversion-based feedforward control relies on an accurate model that describes the inverse system dynamics. The gated recurrent unit (GRU), which is a recent architecture in recurrent neural networks, is a strong candidate for obtaining such a model from data. However, due to their black-box nature, GRUs face challenges such as limited interpretability and vulnerability to overfitting. Recently, physics-guided neural networks (PGNNs) have been introduced, which integrate the prior physical model structure into the prediction process. This approach not only improves training convergence, but also facilitates the learning of a physics-based model. In this work, we integrate a GRU in the PGNN framework to obtain a PG-GRU, based on which we adopt a two-step approach to feedforward control design. First, we adopt stable inversion techniques to design a stable linear model of the inverse dynamics. Then, a GRU trained on the residual is tailored to inverse system identification. The resulting PG-GRU feedforward controller is validated by means of real-life experiments on a two-mass spring-damper system, where it demonstrates roughly a two-fold improvement compared to the linear feedforward and a preview-based GRU feedforward in terms of the integral absolute error.