Practical considerations in reinforcement learning-based MPC for mobile robots

Riccardo Busetto, Valentina Breschi, Giulio Vaccari, Simone Formentin (Corresponding author)

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Abstract

In mobile robot applications, the trajectory tracking task hides several difficulties, including the choice of the setpoint and the search for an acceptable trade-off between performance and computational constraints. In this work, we discuss practical issues of a Reinforcement Learning (RL) based Model Predictive Control (MPC) tuning approach by focusing on a specific mobile robot application, where the objective is to maximize the velocity, while keeping the robot within the track bounds. Among others, we show that softening the latter constraints allows us to obtain a RL-tuned tracking controller with the same performance of an economic nonlinear MPC formulation, but requiring significantly less computational resources.

Original languageEnglish
Pages (from-to)5787-5792
Number of pages6
JournalIFAC-PapersOnLine
Volume56
Issue number2
DOIs
Publication statusPublished - 2023
Event22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023
Conference number: 22
https://www.ifac2023.org/

Funding

This project was partially supported by the Italian Ministry of University and Research under the PRIN'17 project “Data-driven learning of constrained control systems”, contract no. 2017J89ARP.

FundersFunder number
Ministero dell’Istruzione, dell’Università e della Ricerca2017J89ARP

    Keywords

    • Mobile Robots
    • Model Predictive Control (MPC)
    • Reinforcement learning (RL)

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