A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation
This addresses the problem of optimizing communication and control efficiency for industrial systems, but it appears incremental as it builds on existing joint design concepts with a new learning method.
The paper tackles the joint design of event-triggered control and energy-efficient resource allocation in 5G Industrial Cyber-Physical Systems, proposing a hierarchical reinforcement learning approach that significantly reduces actuator updates and downlink power consumption in simulations.
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 \textcolor{blue}{with 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.