Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC

Abstract

Model-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully 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. However, there are no easy-to-use, flexible, and freely available open-source tools that support the development and straightforward solution to these problems. This article outlines the basic ideas and principles behind an easy-to-use Python toolbox that allows to solve machine-learning-supported optimization, model predictive control, and estimation problems quickly and efficiently. The toolbox leverages state-of-the-art machine learning libraries to train components used to define the problem. Machine learning can be used for a broad spectrum of problems, ranging from model predictive control for stabilization, set point tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick generation of code for embedded model predictive control 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.