# Learning Exactly Linearizable Deep Dynamics Models

arXiv: https://arxiv.org/abs/2311.18261 · HTML: https://mlsanity.com/paper/2311.18261/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Input Convex Neural Networks](https://mlsanity.com/paper/1609.07152/) (2016)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Optimal Control Via Neural Networks: A Convex Approach](https://mlsanity.com/paper/1805.11835/) (2018)
- [Integral Control Barrier Functions for Dynamically Defined Control Laws](https://mlsanity.com/paper/2006.16936/) (2020)
- [Linearized Gaussian Processes for Fast Data-driven Model Predictive Control](https://mlsanity.com/paper/1812.10579/) (2018)
- [Structured Hammerstein-Wiener Model Learning for Model Predictive Control](https://mlsanity.com/paper/2107.04247/) (2021)

## Built on by (most cited first, 3 of 3)

- [Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control](https://mlsanity.com/paper/2509.19869/) (2025)
- [Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks](https://mlsanity.com/paper/2510.04591/) (2025)
- [Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control](https://mlsanity.com/paper/2601.18313/) (2026)

## Abstract (arXiv)

Research on control using models based on machine-learning methods has now shifted to the practical engineering stage. Achieving high performance and theoretically guaranteeing the safety of the system is critical for such applications. In this paper, we propose a learning method for exactly linearizable dynamical models that can easily apply various control theories to ensure stability, reliability, etc., and to provide a high degree of freedom of expression. As an example, we present a design that combines simple linear control and control barrier functions. The proposed model is employed for the real-time control of an automotive engine, and the results demonstrate good predictive performance and stable control under constraints.