# On feedforward control using physics-guided neural networks: Training cost regularization and optimized initialization

arXiv: https://arxiv.org/abs/2201.12088 · HTML: https://mlsanity.com/paper/2201.12088/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling](https://mlsanity.com/paper/1710.11431/) (2017)
- [Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling](https://mlsanity.com/paper/1911.02682/) (2019)
- [On the Initialization of Nonlinear LFR Model Identification with the Best Linear Approximation](https://mlsanity.com/paper/2004.05040/) (2020)
- [Physics-Guided Neural Networks for Inversion-based Feedforward Control applied to Linear Motors](https://mlsanity.com/paper/2103.06092/) (2021)

## Built on by (most cited first, 2 of 2)

- [Orthogonal projection-based regularization for efficient model augmentation](https://mlsanity.com/paper/2501.05842/) (2025)
- [PE-MHL: Physics-Encoded Modular Hybrid Layers for Scalable Learning of Complex Systems](https://mlsanity.com/paper/2606.04290/) (2026)

## Abstract (arXiv)

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.