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

by   Johannes Pohlodek, et al.

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.


page 1

page 9

page 12

page 15

page 16


Probabilistic Iterative LQR for Short Time Horizon MPC

Optimal control is often used in robotics for planning a trajectory to a...

Structured Hammerstein-Wiener Model Learning for Model Predictive Control

This paper aims to improve the reliability of optimal control using mode...

Learning Model-Based Vehicle-Relocation Decisions for Real-Time Ride-Sharing: Hybridizing Learning and Optimization

Large-scale ride-sharing systems combine real-time dispatching and routi...

Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines

Homogeneous charge compression ignition (HCCI) is a futuristic combustio...

AI Enhanced Control Engineering Methods

AI and machine learning based approaches are becoming ubiquitous in almo...

Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization

Decision making algorithms are used in a multitude of different applicat...

Please sign up or login with your details

Forgot password? Click here to reset