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 language | English |
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Pages (from-to) | 5787-5792 |
Number of pages | 6 |
Journal | IFAC-PapersOnLine |
Volume | 56 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2023 |
Event | 22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan Duration: 9 Jul 2023 → 14 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.
Funders | Funder number |
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Ministero dell’Istruzione, dell’Università e della Ricerca | 2017J89ARP |
Keywords
- Mobile Robots
- Model Predictive Control (MPC)
- Reinforcement learning (RL)