论文标题
通过HILO-MPC对机器学习支持的最佳控制和估计方法的灵活开发和评估
Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC
论文作者
论文摘要
在许多工程应用中,已经使用了基于模型的监视和控制方法,例如模型预测控制,最佳状态和参数估计。描述动态,约束和所需性能标准的模型是基于模型的方法的基础。多亏了数字化的最新技术进步,诸如深度学习和计算能力之类的机器学习方法,人们对使用机器学习方法以及基于模型的方法进行控制和估算引起了人们的兴趣。用于基于模型的控制和优化的机器学习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,该工具箱允许快速有效地解决机器学习支持的优化,模型预测性控制和估计问题。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。它允许有效地解决所得的优化问题。机器学习可用于广泛的任务,从模型预测控制,设定点跟踪,路径跟踪和轨迹跟踪到移动地平线估计和卡尔曼过滤。对于线性系统,它可以为嵌入式MPC应用程序提供快速代码生成。 HILO-MPC具有灵活性和适应性,使其特别适合研究和基本发展任务。由于其简单性和许多已经实施的示例,它也是一种强大的教学工具。可用性是下划线的,其中提供了一系列申请示例。
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.