# Model-Free Design of Control Systems over Wireless Fading Channels

arXiv: https://arxiv.org/abs/2009.01751 · HTML: https://mlsanity.com/paper/2009.01751/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach](https://mlsanity.com/paper/2210.00673/) (2022)
- [Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control](https://mlsanity.com/paper/2109.12562/) (2021)
- [Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints](https://mlsanity.com/paper/2403.16855/) (2024)
- [Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2311.16895/) (2023)
- [Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control](https://mlsanity.com/paper/2212.13141/) (2022)
- [Communication-Control Codesign for Large-Scale Wireless Networked Control Systems](https://mlsanity.com/paper/2410.11316/) (2024)

## Abstract (arXiv)

Wireless control systems replace traditional wired communication with wireless networks to exchange information between actuators, plants and sensors in a control system. The noise in wireless channels renders ideal control policies suboptimal, and their performance is moreover directly dependent on the way in which wireless resources are allocated between control loops. Proper design of the control policy and the resource allocation policy based on both plant states and wireless fading states is then critical to achieve good performance. The resulting problem of co-designing control-aware resource allocation policies and communication-aware controllers, however, is challenging due to its infinite dimensionality, existence of system constraints and need for explicit knowledge of the plants and wireless network models. To overcome those challenges, we rely on constrained reinforcement learning algorithms to propose a model-free approach to the design of wireless control systems. We demonstrate the near optimality of control system performance and stability using near-universal policy parametrizations and present a practical model-free algorithm to learn the co-design policy. Numerical experiments show the strong performance of learned policies over baseline solutions.