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.

数据科学和机器学习的进步导致非线性动力系统建模和仿真方面的重大改进。现在可以准确预测天气、疾病模型或股票市场等复杂系统。预测方法经常被宣传为对控制有用，但由于系统复杂性更高、需要更大的数据集和增加的建模工作量，具体细节经常得不到解答。换句话说，自治系统的代理建模比控制系统容易得多。在本文中，我们提出了 QuaSiModO（量化-模拟-建模-优化）框架，以将任意预测模型转换为控制系统，从而使数据驱动的代理模型的巨大进步可用于控制。我们的主要贡献是我们通过自动化动力学来交易控制效率——这会产生混合整数控制问题——以获得任意的、随时可用的自主代理建模技术。然后，我们利用混合整数优化的最新结果来恢复原始问题的复杂性。QuaSiModO 的优点是数据需求相对于控制维度呈线性增长，性能保证完全依赖于所用预测模型的准确性，