  Signal Temporal Logic (STL) has become a popular tool for expressing formal
requirements of Cyber-Physical Systems (CPS). The problem of verifying STL
properties of neural network-controlled CPS remains a largely unexplored
problem. In this paper, we present a model for the verification of Neural
Network (NN) controllers for general STL specifications using a custom neural
architecture where we map an STL formula into a feed-forward neural network
with ReLU activation. In the case where both our plant model and the controller
are ReLU-activated neural networks, we reduce the STL verification problem to
reachability in ReLU neural networks. We also propose a new approach for neural
network controllers with general activation functions; this approach is a sound
and complete verification approach based on computing the Lipschitz constant of
the closed-loop control system. We demonstrate the practical efficacy of our
techniques on a number of examples of learning-enabled control systems.
