# Empowering Optimal Control with Machine Learning: A Perspective from Model Predictive Control

arXiv: https://arxiv.org/abs/2205.07990 · HTML: https://mlsanity.com/paper/2205.07990/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2022.

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

- [Offline Supervised Learning V.S. Online Direct Policy Optimization: A Comparative Study and A Unified Training Paradigm for Neural Network-Based Optimal Feedback Control](https://mlsanity.com/paper/2211.15930/) (2022)
- [Unifying back-propagation and forward-forward algorithms through model predictive control](https://mlsanity.com/paper/2409.19561/) (2024)
- [Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems](https://mlsanity.com/paper/2607.16177/) (2026)

## Abstract (arXiv)

Solving complex optimal control problems have confronted computational challenges for a long time. Recent advances in machine learning have provided us with new opportunities to address these challenges. This paper takes model predictive control, a popular optimal control method, as the primary example to survey recent progress that leverages machine learning techniques to empower optimal control solvers. We also discuss some of the main challenges encountered when applying machine learning to develop more robust optimal control algorithms.