# A Hamilton-Jacobi Approach for Nonlinear Model Predictive Control in Applications with Navigational Uncertainty

arXiv: https://arxiv.org/abs/2503.23603 · HTML: https://mlsanity.com/paper/2503.23603/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2025.

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

- [Tiny Recursive Control: Iterative Reasoning for Efficient Optimal Control](https://mlsanity.com/paper/2512.16824/) (2025)
- [Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problems](https://mlsanity.com/paper/2606.20394/) (2026)

## Abstract (arXiv)

This paper introduces a novel methodology that leverages the Hamilton-Jacobi solution to enhance non-linear model predictive control (MPC) in scenarios affected by navigational uncertainty. Using Hamilton-Jacobi-Theoretic approach, a methodology to improve trajectory tracking accuracy among uncertainties and non-linearities is formulated. This paper seeks to overcome the challenge of real-time computation of optimal control solutions for Model Predictive Control applications by leveraging the Hamilton-Jacobi solution in the vicinity of a nominal trajectory. The efficacy of the proposed methodology is validated within a chaotic system of the planar circular restricted three-body problem.