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This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of control systems 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 one control system is controlled, while the rest of the control systems 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 <inline-formula> <tex-math notation="LaTeX">$2\times$ </tex-math></inline-formula> more number of actuators compared to an event-triggered scheduling baseline with Kalman filtering and frequency division multiple access, which is <inline-formula> <tex-math notation="LaTeX">$18\times$ </tex-math></inline-formula> larger than a round-robin scheduling baseline.

Predictive Control and Communication Co-Design via Two-Way Gaussian Process Regression and AoI-Aware Scheduling