On the Universal Transformation of DataDriven Models to Control Systems
Abstract
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 (QuantizationSimulationModelingOptimization) to transform arbitrary predictive models into control systems and thus render the tremendous advances in datadriven surrogate modeling accessible for control. Our main contribution is that we trade control efficiency by autonomizing the dynamics  which yields mixedinteger control problems  to gain access to arbitrary, readytouse autonomous surrogate modeling techniques. We then recover the complexity of the original problem by leveraging recent results from mixedinteger 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.
 Publication:

arXiv eprints
 Pub Date:
 February 2021
 DOI:
 10.48550/arXiv.2102.04722
 arXiv:
 arXiv:2102.04722
 Bibcode:
 2021arXiv210204722P
 Keywords:

 Mathematics  Optimization and Control;
 Computer Science  Machine Learning;
 Electrical Engineering and Systems Science  Systems and Control
 EPrint:
 doi:10.1016/j.automatica.2022.110840