# Algorithmic design and implementation considerations of deep MPC

arXiv: https://arxiv.org/abs/2511.17233 · HTML: https://mlsanity.com/paper/2511.17233/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Model Reference Adaptive Control](https://mlsanity.com/paper/1909.08602/) (2019)
- [Safe Hierarchical Model Predictive Control and Planning for Autonomous Systems](https://mlsanity.com/paper/2203.14269/) (2022)
- [Deep Model Predictive Control with Stability Guarantees](https://mlsanity.com/paper/2104.07171/) (2021)
- [Sparse and Constrained Stochastic Predictive Control for Networked Systems](https://mlsanity.com/paper/1706.01792/) (2017)
- [Unmatched uncertainty mitigation through neural network supported model predictive control](https://mlsanity.com/paper/2304.11315/) (2023)
- [CropNav: a Framework for Autonomous Navigation in Real Farms](https://mlsanity.com/paper/2411.10974/) (2024)
- [Centralized model predictive control with distributed adaptation](https://mlsanity.com/paper/2004.02358/) (2020)
- [Reference tracking stochastic model predictive control over unreliable channels and bounded control actions](https://mlsanity.com/paper/2006.04367/) (2020)
- [A novel constraint tightening approach for robust data-driven predictive control](https://mlsanity.com/paper/2203.07055/) (2022)
- [Polynomial Chaos-based Stochastic Model Predictive Control: An Overview and Future Research Directions](https://mlsanity.com/paper/2406.10734/) (2024)

## Abstract (arXiv)

Deep Model Predictive Control (Deep MPC) is an evolving field that integrates model predictive control and deep learning. This manuscript is focused on a particular approach, which employs deep neural network in the loop with MPC. This class of approaches distributes control authority between a neural network and an MPC controller, in such a way that the neural network learns the model uncertainties while the MPC handles constraints. The approach is appealing because training data collected while the system is in operation can be used to fine-tune the neural network, and MPC prevents unsafe behavior during those learning transients. This manuscript explains implementation challenges of Deep MPC, algorithmic way to distribute control authority and argues that a poor choice in distributing control authority may lead to poor performance. A reason of poor performance is explained through a numerical experiment on a four-wheeled skid-steer dynamics.