数据驱动模型到控制系统的通用转换
On the Universal Transformation of Data-Driven Models to Control Systems
摘要：
数据科学和机器学习的进步使得非线性动力系统的建模和仿真有了显著的改进。如今，人们可以对天气、疾病模型或股票市场等复杂系统进行准确预测。预测方法通常被宣传为对控制有用，但由于系统复杂性较高，需要更大的数据集和建模工作量增加，因此具体细节经常没有得到回答。换句话说，自治系统的代理建模比控制系统的代理建模容易得多。在本文中，我们提出的框架EQUISIModO（量化仿真建模优化）任意预测模型转化为控制系统，从而使巨大的进步，数据驱动的代理建模访问控制。我们的主要贡献是，我们的贸易控制效率的动力学-这会产生混合整数控制问题-获得任意的，随时可用的自主代理建模技术。然后，我们恢复的复杂性，利用最近的结果，从混合整数优化的原始问题。的优点是线性增加的数据要求相对于控制维度，性能保证，完全依赖于使用中的预测模型的准确性，并在控制理论解决复杂的控制问题的先验知识要求很少。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
