  Wireless control systems replace traditional wired communication with
wireless networks to exchange information between actuators, plants and sensors
in a control system. The noise in wireless channels renders ideal control
policies suboptimal, and their performance is moreover directly dependent on
the way in which wireless resources are allocated between control loops. Proper
design of the control policy and the resource allocation policy based on both
plant states and wireless fading states is then critical to achieve good
performance. The resulting problem of co-designing control-aware resource
allocation policies and communication-aware controllers, however, is
challenging due to its infinite dimensionality, existence of system constraints
and need for explicit knowledge of the plants and wireless network models. To
overcome those challenges, we rely on constrained reinforcement learning
algorithms to propose a model-free approach to the design of wireless control
systems. We demonstrate the near optimality of control system performance and
stability using near-universal policy parametrizations and present a practical
model-free algorithm to learn the co-design policy. Numerical experiments show
the strong performance of learned policies over baseline solutions.
