  Neural networks are being increasingly applied to control and decision-making
for learning-enabled cyber-physical systems (LE-CPSs). They have shown
promising performance without requiring the development of complex physical
models; however, their adoption is significantly hindered by the concerns on
their safety, robustness, and efficiency. In this work, we propose COCKTAIL, a
novel design framework that automatically learns a neural network-based
controller from multiple existing control methods (experts) that could be
either model-based or neural network-based. In particular, COCKTAIL first
performs reinforcement learning to learn an optimal system-level adaptive
mixing strategy that incorporates the underlying experts with
dynamically-assigned weights and then conducts a teacher-student distillation
with probabilistic adversarial training and regularization to synthesize a
student neural network controller with improved control robustness (measured by
a safe control rate metric with respect to adversarial attacks or measurement
noises), control energy efficiency, and verifiability (measured by the
computation time for verification). Experiments on three non-linear systems
demonstrate significant advantages of our approach on these properties over
various baseline methods.
