  This article studies the joint problem of uplink-downlink scheduling and
power allocation for controlling a large number of actuators that upload their
states to remote controllers and download control actions over wireless links.
To overcome the lack of wireless resources, we propose a machine learning-based
solution, where only a fraction of actuators is controlled, while the rest of
the actuators are actuated by locally predicting the missing state and/or
action information using the previous uplink and/or downlink receptions via a
Gaussian process regression (GPR). This GPR prediction credibility is
determined using the age-of-information (AoI) of the latest reception.
Moreover, the successful reception is affected by the transmission power,
mandating a co-design of the communication and control operations. To this end,
we formulate a network-wide minimization problem of the average AoI and
transmission power under communication reliability and control stability
constraints. To solve the problem, we propose a dynamic control algorithm using
the Lyapunov drift-plus-penalty optimization framework. Numerical results
corroborate that the proposed algorithm can stably control $2$x more number of
actuators compared to an event-triggered scheduling baseline with Kalman
filtering and frequency division multiple access, which is $18$x larger than a
round-robin scheduling baseline.
