# A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation

arXiv: https://arxiv.org/abs/2205.07070 · HTML: https://mlsanity.com/paper/2205.07070/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 14 May 2022.

## Builds on (most cited first, 12 of 12)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Actor-Critic Reinforcement Learning for Control with Stability Guarantee](https://mlsanity.com/paper/2004.14288/) (2020)
- [DAC: The Double Actor-Critic Architecture for Learning Options](https://mlsanity.com/paper/1904.12691/) (2019)
- [Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems](https://mlsanity.com/paper/1809.05149/) (2018)
- [DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling](https://mlsanity.com/paper/1803.02998/) (2018)
- [Learning Event-triggered Control from Data through Joint Optimization](https://mlsanity.com/paper/2008.04712/) (2020)
- [Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee](https://mlsanity.com/paper/2011.06882/) (2020)
- [Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems](https://mlsanity.com/paper/1811.00409/) (2018)
- [On the Latency, Rate and Reliability Tradeoff in Wireless Networked Control Systems for IIoT](https://mlsanity.com/paper/2007.00256/) (2020)
- [Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks](https://mlsanity.com/paper/2005.04213/) (2020)

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

- [Communication-Control Codesign for Large-Scale Wireless Networked Control Systems](https://mlsanity.com/paper/2410.11316/) (2024)
- [Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems](https://mlsanity.com/paper/2604.26126/) (2026)

## Abstract (arXiv)

In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5G) wireless network. We formally state the problem as a multi-objective optimization one, aiming to minimize the number of updates on the actuators' input and the power consumption in the downlink transmission. To address the problem, we propose a model-free hierarchical reinforcement learning approach textcolorbluewith uniformly ultimate boundedness stability guarantee that learns four policies simultaneously. These policies contain an update time policy on the actuators' input, a control policy, and energy-efficient sub-carrier and power allocation policies. Our simulation results show that the proposed approach can properly control a simulated ICPS and significantly decrease the number of updates on the actuators' input as well as the downlink power consumption.