论文标题
使用物理引导的神经网络进行前馈控制：培训成本正规化和优化的初始化
On feedforward control using physics-guided neural networks: Training cost regularization and optimized initialization
论文作者
论文摘要
基于模型的前馈控制器的性能通常受反系统动力学模型的精度限制。物理学指导的神经网络（PGNN）最近提出了与神经网络并行合作的，该模型是作为实现识别逆动力学高精度的一种方法。但是，当与物理模型并行使用时，神经网络的柔性性质可能会产生过度参数化，从而导致训练过程中的参数漂移。这种漂移可能导致物理模型的参数与其物理值相对应，这会增加PGNN对训练数据中不存在的操作条件的脆弱性。为了解决这个问题，本文通过确定的物理参数提出了一种正则化方法，并结合了优化的训练初始化，从而改善了训练收敛。正规化的PGNN框架已在现实生活中的工业线性电动机上进行了验证，在该电动机中，它提供了更好的跟踪准确性和外推。
Performance of model-based feedforward controllers is typically limited by the accuracy of the inverse system dynamics model. Physics-guided neural networks (PGNN), where a known physical model cooperates in parallel with a neural network, were recently proposed as a method to achieve high accuracy of the identified inverse dynamics. However, the flexible nature of neural networks can create overparameterization when employed in parallel with a physical model, which results in a parameter drift during training. This drift may result in parameters of the physical model not corresponding to their physical values, which increases vulnerability of the PGNN to operating conditions not present in the training data. To address this problem, this paper proposes a regularization method via identified physical parameters, in combination with an optimized training initialization that improves training convergence. The regularized PGNN framework is validated on a real-life industrial linear motor, where it delivers better tracking accuracy and extrapolation.
