# Data-driven control and transfer learning using neural canonical control structures*

arXiv: https://arxiv.org/abs/2302.04042 · HTML: https://mlsanity.com/paper/2302.04042/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [META-SMGO-Delta: similarity as a prior in black-box optimization](https://mlsanity.com/paper/2305.00438/) (2023)

## Abstract (arXiv)

An indirect data-driven control and transfer learning approach based on a data-driven feedback linearization with neural canonical control structures is proposed. An artificial neural network auto-encoder structure trained on recorded sensor data is used to approximate state and input transformations for the identification of the sampled-data system in Brunovsky canonical form. The identified transformations, together with a designed trajectory controller, can be transferred to a system with varied parameters, where the neural network weights are adapted using newly collected recordings. The proposed approach is demonstrated using an academic and an industrially motivated example.