# Learning disturbance models for offset-free reference tracking

arXiv: https://arxiv.org/abs/2312.11409 · HTML: https://mlsanity.com/paper/2312.11409/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2023.

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

- [Statistical Learning of Contractive Dynamical Representations for Composite Adaptive Control](https://mlsanity.com/paper/2609.35758/) (2026)

## Abstract (arXiv)

This work presents a nonlinear control framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which compensates for input disturbances and model-plant mismatch. Our approach generalizes the well-established method of using an observer to estimate a constant disturbance to allow tracking constant setpoints with zero steady-state error. In this paper, the disturbance model is generalized to a nonlinear static function of the plant's state and command input, learned online, so as to perfectly track time-varying reference trajectories under certain assumptions on the model and provided that future reference samples are available. We compare our approach with the classical constant disturbance model in numerical simulations, showing its superiority.