# Self-Tuning Tube-based Model Predictive Control

arXiv: https://arxiv.org/abs/2210.00502 · HTML: https://mlsanity.com/paper/2210.00502/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2022.

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

- [Receding-Horizon Control via Drifting Models](https://mlsanity.com/paper/2604.04528/) (2026)
- [Sub-linear Regret in Adaptive Model Predictive Control](https://mlsanity.com/paper/2310.04842/) (2023)

## Abstract (arXiv)

We present Self-Tuning Tube-based Model Predictive Control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the least-squares estimator and polytopic tubes. Our algorithm leverages concentration results to bound the system uncertainty set with prescribed confidence, and guarantees robust constraint satisfaction for this set, along with recursive feasibility and input-to-state stability. Persistence of excitation is ensured without compromising the algorithm's asymptotic performance or increasing its computational complexity. We demonstrate the performance of our algorithm using numerical experiments.