# Closed-loop Performance Optimization of Model Predictive Control with Robustness Guarantees

arXiv: https://arxiv.org/abs/2403.04655 · HTML: https://mlsanity.com/paper/2403.04655/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2024.

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

- [Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees](https://mlsanity.com/paper/2512.04781/) (2025)
- [Pick-to-Learn Calibration of an MPC Policy for an Origin-to-Destination Flight Problem](https://mlsanity.com/paper/2607.16084/) (2026)

## Abstract (arXiv)

Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications. We propose a principled way to tune the cost function and the constraints of linear MPC schemes to improve the closed-loop performance and robust constraint satisfaction on uncertain nonlinear dynamics with additive noise. The tuning is performed using a novel MPC tuning algorithm based on backpropagation developed in our earlier work. Using the scenario approach, we provide probabilistic bounds on the likelihood of closed-loop constraint violation over a finite horizon. We showcase the effectiveness of the proposed method on linear and nonlinear simulation examples.