  The advances in data science and machine learning have resulted in
significant improvements regarding the modeling and simulation of nonlinear
dynamical systems. It is nowadays possible to make accurate predictions of
complex systems such as the weather, disease models or the stock market.
Predictive methods are often advertised to be useful for control, but the
specifics are frequently left unanswered due to the higher system complexity,
the requirement of larger data sets and an increased modeling effort. In other
words, surrogate modeling for autonomous systems is much easier than for
control systems. In this paper we present the framework QuaSiModO
(Quantization-Simulation-Modeling-Optimization) to transform arbitrary
predictive models into control systems and thus render the tremendous advances
in data-driven surrogate modeling accessible for control. Our main contribution
is that we trade control efficiency by autonomizing the dynamics - which yields
mixed-integer control problems - to gain access to arbitrary, ready-to-use
autonomous surrogate modeling techniques. We then recover the complexity of the
original problem by leveraging recent results from mixed-integer optimization.
The advantages of QuaSiModO are a linear increase in data requirements with
respect to the control dimension, performance guarantees that rely exclusively
on the accuracy of the predictive model in use, and little prior knowledge
requirements in control theory to solve complex control problems.
