# Stability and performance of stochastic predictive control

arXiv: https://arxiv.org/abs/1304.2581 · HTML: https://mlsanity.com/paper/1304.2581/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2013.

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

- [Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraints](https://mlsanity.com/paper/2304.00569/) (2023)
- [Online Constraint Tightening in Stochastic Model Predictive Control: A Regression Approach](https://mlsanity.com/paper/2310.02942/) (2023)

## Abstract (arXiv)

This article is concerned with stability and performance of controlled stochastic processes under receding horizon policies. We carry out a systematic study of methods to guarantee stability under receding horizon policies via appropriate selections of cost functions in the underlying finite-horizon optimal control problem. We also obtain quantitative bounds on the performance of the system under receding horizon policies as measured by the long-run expected average cost. The results are illustrated with the help of several simple examples.