Efficient Nonlinear Model Predictive Path Integral Control for Stochastic Systems considering Input Constraints
- This paper compares novel methods to efficiently include input constraints using the nonlinear Model Predictive Path Integral (MPPI) approach. The MPPI algorithm solves stochastic optimal control problems and is based on sampled trajectories. MPPI results from the physical path integral framework. Sample-based algorithms are characterized by the fact that they can be computed in parallel and offer the possibility to handle discontinuous dynamics and cost functions. However, using standard MPPI the input costs in the Lagrange term have to be chosen quadratic. This fact is unfavorable for various real applications. Further, in standard nonlinear model predictive control (NMPC) approaches hard box constraints on the control input trajectory can be treated directly. In this contribution, novel architectures based on integrator action are compared. The investigated input constraint MPPI controllers were tested on an autonomous self-balancing vehicle. Therefore both, simulation and real-world experiments are presented. This paper addresses the question of how the MPPI algorithm can be further developed to consider input box constraints. Videos of the self-balancing vehicle are available at: https: https://tinyurl.com/mvn8j7vf
Author: | Hannes HomburgerORCiD, Stefan WirtensohnORCiD, Johannes ReuterORCiD |
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DOI: | https://doi.org/10.23919/ECC57647.2023.10178349 |
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: | 2023/12/18 |
Page Number: | 6 |
Institutes: | Institut für Systemdynamik - ISD |
Relevance: | Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren) |
Open Access?: | Nein |