# Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges

arXiv: https://arxiv.org/abs/2010.09087 · HTML: https://mlsanity.com/paper/2010.09087/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Time-invariant Prefix Coding for LQG Control](https://mlsanity.com/paper/2204.00588/) (2022)
- [RockNet: Distributed Learning on Ultra-Low-Power Devices](https://mlsanity.com/paper/2510.13320/) (2025)
- [Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning](https://mlsanity.com/paper/2605.10482/) (2026)
- [Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices](https://mlsanity.com/paper/2605.15694/) (2026)

## Abstract (arXiv)

Smart manufacturing aims to overcome the limitations of today's rigid assembly lines by making the material flow and manufacturing process more flexible, versatile, and scalable. The main economic drivers are higher resource and cost efficiency as the manufacturers can more quickly adapt to changing market needs and also increase the lifespan of their production sites. The ability to close feedback loops fast and reliably over long distances among mobile robots, remote sensors, and human operators is a key enabler for smart manufacturing. Thus, this article provides a perspective on control and coordination over wireless networks. Based on an analysis of real-world use cases, we identify the main technical challenges that need to be solved to close the large gap between the current state of the art in industry and the vision of smart manufacturing. We discuss to what extent existing control-over-wireless solutions in the literature address those challenges, including our own approach toward a tight integration of control and wireless communication. In addition to a theoretical analysis of closed-loop stability, practical experiments on a cyber-physical testbed demonstrate that our approach supports relevant smart manufacturing scenarios. The article concludes with a discussion of open challenges and future research directions.