# Reference tracking stochastic model predictive control over unreliable channels and bounded control actions

arXiv: https://arxiv.org/abs/2006.04367 · HTML: https://mlsanity.com/paper/2006.04367/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

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

- [Algorithmic design and implementation considerations of deep MPC](https://mlsanity.com/paper/2511.17233/) (2025)

## Abstract (arXiv)

A stochastic model predictive control framework over unreliable Bernoulli communication channels, in the presence of unbounded process noise and under bounded control inputs, is presented for tracking a reference signal. The data losses in the control channel are compensated by a carefully designed transmission protocol, and that of the sensor channel by a dropout compensator. A class of saturated, disturbance feedback policies is proposed for control in the presence of noisy dropout compensation. A reference governor is employed to generate trackable reference trajectories and stability constraints are employed to ensure mean-square boundedness of the reference tracking error. The overall approach yields a computationally tractable quadratic program, which can be iteratively solved online.