通过HILO-MPC灵活开发和评估机器学习支持的最优控制和估计方法
Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC
摘要：
用于监测和控制的基于模型的优化方法，例如模型预测控制和最优状态和参数估计，已经在许多工程应用中使用了几十年。描述动态、约束和期望性能标准的模型是基于模型的方法的基础。由于最近在数字化、机器学习方法（如深度学习）和计算能力方面的技术进步，人们越来越有兴趣将机器学习方法与基于模型的方法一起用于控制和估计。使用机器学习进行基于模型的控制和优化的新方法和理论发现的数量正在迅速增加。本文概述了易于使用的Python工具箱背后的基本思想和原理，该工具箱可以快速有效地解决机器学习支持的优化，模型预测控制和估计问题。该工具箱利用最先进的机器学习库来训练用于定义问题的组件。它允许有效地解决由此产生的优化问题。机器学习可用于广泛的任务，从用于稳定、设定点跟踪、路径跟踪和轨迹跟踪的模型预测控制到移动时域估计和卡尔曼滤波。对于线性系统，它可以为嵌入式MPC应用程序快速生成代码。HILO-MPC具有灵活性和适应性，特别适合研究和基础开发任务。由于它的简单性和许多已经实施的例子，它也是一个强大的教学工具。可用性是强调，提出了一系列的应用程序的例子。
英文摘要：
Model-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model-based approaches. Thanks to recent technological advancements in digitalization, machine learning methods such as deep learning, and computing power, there has been an increasing interest in using machine learning methods alongside model-based approaches for control and estimation. The number of new methods and theoretical findings using machine learning for model-based control and optimization is increasing rapidly. This paper outlines the basic ideas and principles behind an easy-to-use Python toolbox that allows to quickly and efficiently solve machine-learning-supported optimization, model predictive control, and estimation problems. The toolbox leverages state-of-the-art machine learning libraries to train components used to define the problem. It allows to efficiently solve the resulting optimization problems. Machine learning can be used for a broad spectrum of tasks, ranging from model predictive control for stabilization, setpoint tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick code generation for embedded MPC applications. HILO-MPC is flexible and adaptable, making it especially suitable for research and fundamental development tasks. Due to its simplicity and numerous already implemented examples, it is also a powerful teaching tool. The usability is underlined, presenting a series of application examples.
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。
