  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.
