# Stabilizing Stochastic Predictive Control under Bernoulli Dropouts

arXiv: https://arxiv.org/abs/1603.06234 · HTML: https://mlsanity.com/paper/1603.06234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2016.

## Built on by (most cited first, 1 of 1)

- [Unmatched uncertainty mitigation through neural network supported model predictive control](https://mlsanity.com/paper/2304.11315/) (2023)

## Abstract (arXiv)

This article presents tractable and recursively feasible optimization-based controllers for stochastic linear systems with bounded controls. The stochastic noise in the plant is assumed to be additive, zero mean and fourth moment bounded, and the control values transmitted over an erasure channel. Three different transmission protocols are proposed having different requirements on the storage and computational facilities available at the actuator. We optimize a suitable stochastic cost function accounting for the effects of both the stochastic noise and the packet dropouts over affine saturated disturbance feedback policies. The proposed controllers ensure mean square boundedness of the states in closed-loop for all positive values of control bounds and any non-zero probability of successful transmission over a noisy control channel.