# Unmatched uncertainty mitigation through neural network supported model predictive control

arXiv: https://arxiv.org/abs/2304.11315 · HTML: https://mlsanity.com/paper/2304.11315/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 22 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Cautious Model Predictive Control using Gaussian Process Regression](https://mlsanity.com/paper/1705.10702/) (2017)
- [WayFAST: Navigation with Predictive Traversability in the Field](https://mlsanity.com/paper/2203.12071/) (2022)
- [Deep Model Reference Adaptive Control](https://mlsanity.com/paper/1909.08602/) (2019)
- [Deep Model Predictive Control with Stability Guarantees](https://mlsanity.com/paper/2104.07171/) (2021)
- [Stochastic Predictive Control under Intermittent Observations and Unreliable Actions](https://mlsanity.com/paper/1911.03617/) (2019)
- [Stabilizing Stochastic Predictive Control under Bernoulli Dropouts](https://mlsanity.com/paper/1603.06234/) (2016)

## Built on by (most cited first, 2 of 2)

- [Algorithmic design and implementation considerations of deep MPC](https://mlsanity.com/paper/2511.17233/) (2025)
- [A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing](https://mlsanity.com/paper/2607.18164/) (2026)

## Abstract (arXiv)

This paper presents a deep learning based model predictive control (MPC) algorithm for systems with unmatched and bounded state-action dependent uncertainties of unknown structure. We utilize a deep neural network (DNN) as an oracle in the underlying optimization problem of learning based MPC (LBMPC) to estimate unmatched uncertainties. Generally, non-parametric oracles such as DNN are considered difficult to employ with LBMPC due to the technical difficulties associated with estimation of their coefficients in real time. We employ a dual-timescale adaptation mechanism, where the weights of the last layer of the neural network are updated in real time while the inner layers are trained on a slower timescale using the training data collected online and selectively stored in a buffer. Our results are validated through a numerical experiment on the compression system model of jet engine. These results indicate that the proposed approach is implementable in real time and carries the theoretical guarantees of LBMPC.