# Structured Hammerstein-Wiener Model Learning for Model Predictive Control

arXiv: https://arxiv.org/abs/2107.04247 · HTML: https://mlsanity.com/paper/2107.04247/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 9 Jul 2021.

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

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [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)
- [Input Convex Neural Networks for Building MPC](https://mlsanity.com/paper/2011.13227/) (2020)
- [Linearized Gaussian Processes for Fast Data-driven Model Predictive Control](https://mlsanity.com/paper/1812.10579/) (2018)

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

- [Learning Exactly Linearizable Deep Dynamics Models](https://mlsanity.com/paper/2311.18261/) (2023)
- [Learning Stabilizable Deep Dynamics Models](https://mlsanity.com/paper/2203.09710/) (2022)
- [A Penalty-Based Guardrail Algorithm for Non-Decreasing Optimization with Inequality Constraints](https://mlsanity.com/paper/2405.01984/) (2024)
- [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)

This paper aims to improve the reliability of optimal control using models constructed by machine learning methods. Optimal control problems based on such models are generally non-convex and difficult to solve online. In this paper, we propose a model that combines the Hammerstein-Wiener model with input convex neural networks, which have recently been proposed in the field of machine learning. An important feature of the proposed model is that resulting optimal control problems are effectively solvable exploiting their convexity and partial linearity while retaining flexible modeling ability. The practical usefulness of the method is examined through its application to the modeling and control of an engine airpath system.