  While Remote control over wireless connections is a key enabler for scalable
control systems consisting of multiple actuator-sensor pairs, i.e., control
systems, it entails two technical challenges. Due to the lack of wireless
resources, only a limited number of control systems can be served, making the
state observations outdated. Further, even after scheduling, the state
observations received through wireless channels are distorted, hampering
control stability. To address these issues, in this article we propose a
scheduling algorithm that guarantees the age-of-information (AoI) of the last
received states. Meanwhile, for non-scheduled sensor-actuator pairs, we propose
a machine learning (ML) aided predictive control algorithm, in which states are
predicted using a Gaussian process regression (GPR). Since the GPR prediction
credibility decreases with the AoI of the input data, both predictive control
and AoI-based scheduler should be co-designed. Hence, we formulate a joint
scheduling and transmission power optimization via the Lyapunov optimization
framework. Numerical simulations corroborate that the proposed co-designed
predictive control and AoI based scheduling achieves lower control errors,
compared to a benchmark scheme using a round-robin scheduler without state
prediction.
