  There is a growing debate on whether the future of feedback control systems
will be dominated by data-driven or model-driven approaches. Each of these two
approaches has their own complimentary set of advantages and disadvantages,
however, only limited attempts have, so far, been developed to bridge the gap
between them. To address this issue, this paper introduces a method to bound
the worst-case error between feedback control policies based upon model
predictive control (MPC) and neural networks (NNs). This result is leveraged
into an approach to automatically synthesize MPC policies minimising the
worst-case error with respect to a NN. Numerical examples highlight the
application of the bounds, with the goal of the paper being to encourage a more
quantitative understanding of the relationship between data-driven and
model-driven control.
