# Anytime Control under Practical Communication Model

arXiv: https://arxiv.org/abs/2012.00962 · HTML: https://mlsanity.com/paper/2012.00962/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

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

- [Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control](https://mlsanity.com/paper/2109.12562/) (2021)
- [Wireless Human-Machine Collaboration in Industry 5.0](https://mlsanity.com/paper/2410.14153/) (2024)

## Abstract (arXiv)

We investigate a novel anytime control algorithm for wireless networked control with random dropouts. The controller computes sequences of tentative future control commands using time-varying (Markovian) computational resources. The sensor-controller and controller-actuator channel states are spatial- and time-correlated, and are modeled as a multi-state Markov process. To compensate for the effect of packet dropouts, a dual-buffer mechanism is proposed. We develop a novel cycle-cost-based approach to obtain the stability conditions on the nonlinear plant, controller, network and computational resources.