MPPI Control of a Self-Balancing Vehicle Employing Subordinated Control Loops
- Recently published nonlinear model-based control approaches achieve impressive performances in complex real- world applications. However, due to model-plant mismatches and unforeseen disturbances, the model-based controller’s per- formance is limited in full-scale applications. In most applica- tions, low-level control loops mitigate the model-plant mismatch and the sensitivity to disturbances. But what is the influence of these low-level control loops? In this paper, we present the model predictive path integral (MPPI) control of a self- balancing vehicle and investigate the influence of subordinate control loops on closed-loop performance. Therefore, simulation and full-scale experiments are performed and analyzed. Subor- dinate control loops empower the MPPI controller because they dampen the influence of disturbances, and thus improve the model’s accuracy. This is the basis for the successful application of model-based control approaches in real-world systems. All in all, a model is used to design a low-level controller, then its closed-loop behavior is determined, and this model is used within the superimposed MPPI control loop – modeling for control and vice versa.
Author: | Hannes HomburgerORCiD, Stefan WirtensohnORCiD, Johannes ReuterORCiD |
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DOI: | https://doi.org/10.23919/ECC57647.2023.10178131 |
ISBN: | 978-3-907144-08-4 |
ISBN: | 978-1-6654-6531-1 |
Parent Title (English): | Proceedings of the 21th European Control Conference (ECC), June 13-16, 2023, Bucharest, Romania |
Publisher: | IEEE |
Document Type: | Conference Proceeding |
Language: | English |
Year of Publication: | 2023 |
Release Date: | 2023/12/11 |
Page Number: | 6 |
Institutes: | Institut für Systemdynamik - ISD |
Relevance: | Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren) |
Open Access?: | Nein |