# Physics-Guided Neural Networks for Inversion-based Feedforward Control applied to Linear Motors

arXiv: https://arxiv.org/abs/2103.06092 · HTML: https://mlsanity.com/paper/2103.06092/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [On feedforward control using physics-guided neural networks: Training cost regularization and optimized initialization](https://mlsanity.com/paper/2201.12088/) (2022)
- [Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics](https://mlsanity.com/paper/2209.12489/) (2022)
- [PE-MHL: Physics-Encoded Modular Hybrid Layers for Scalable Learning of Complex Systems](https://mlsanity.com/paper/2606.04290/) (2026)
- [Certified Feedforward Tracking for Unknown Nonlinear Systems via Invertible Neural Networks](https://mlsanity.com/paper/2608.06419/) (2026)

## Abstract (arXiv)

Ever-increasing throughput specifications in semiconductor manufacturing require operating high-precision mechatronics, such as linear motors, at higher accelerations. In turn this creates higher nonlinear parasitic forces that cannot be handled by industrial feedforward controllers. Motivated by this problem, in this paper we develop a general framework for inversion-based feedforward controller design using physics-guided neural networks (PGNNs). In contrast with black-box neural networks, the developed PGNNs embed prior physical knowledge in the input and hidden layers, which results in improved training convergence and learning of underlying physical laws. The PGNN inversion-based feedforward control framework is validated in simulation on an industrial linear motor, for which it achieves a mean average tracking error twenty times smaller than mass-acceleration feedforward in simulation.