Feature-Based MPPI Control with Applications to Maritime Systems
- In this paper, a novel feature-based sampling strategy for nonlinear Model Predictive Path Integral (MPPI) control is presented. Using the MPPI approach, the optimal feedback control is calculated by solving a stochastic optimal control (OCP) problem online by evaluating the weighted inference of sampled stochastic trajectories. While the MPPI algorithm can be excellently parallelized, the closed-loop performance strongly depends on the information quality of the sampled trajectories. To draw samples, a proposal density is used. The solver’s and thus, the controller’s performance is of high quality if the sampled trajectories drawn from this proposal density are located in low-cost regions of state-space. In classical MPPI control, the explored state-space is strongly constrained by assumptions that refer to the control value’s covariance matrix, which are necessary for transforming the stochastic Hamilton–Jacobi–Bellman (HJB) equation into a linear second-order partial differential equation. To achieve excellent performance even with discontinuous cost functions, in this novel approach, knowledge-based features are introduced to constitute the proposal density and thus the low-cost region of state-space for exploration. This paper addresses the question of how the performance of the MPPI algorithm can be improved using a feature-based mixture of base densities. Furthermore, the developed algorithm is applied to an autonomous vessel that follows a track and concurrently avoids collisions using an emergency braking feature. Therefore, the presented feature-based MPPI algorithm is applied and analyzed in both simulation and full-scale experiments.
Author: | Hannes HomburgerORCiD, Stefan WirtensohnORCiD, Moritz DiehlORCiD, Johannes ReuterORCiD |
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URN: | urn:nbn:de:bsz:kon4-opus4-32230 |
DOI: | https://doi.org/10.3390/machines10100900 |
ISSN: | 2075-1702 |
Parent Title (English): | Machines |
Volume: | 10 |
Publisher: | MDPI |
Place of publication: | Basel |
Document Type: | Article |
Language: | English |
Year of Publication: | 2022 |
Release Date: | 2022/10/14 |
Tag: | Sample-based nonlinear model predictive control; Stochastic system dynamics; Nonlinear model predictive control; Maritime systems; Collision avoidance |
Issue: | 10 |
Page Number: | 24 Seiten |
Article Number: | 900 |
Note: | Corresponding author: Hannes Homburger |
Note: | This paper is an extended version of our paper published in Proceedings of the 6th IEEE Conference on Control Technology and Applications (CCTA), Trieste, Italy, 22–25 August 2022, with the title “Feature-Based Proposal Density Optimization for Nonlinear Model Predictive Path Integral Control”. |
Institutes: | Institut für Systemdynamik - ISD |
Open Access?: | Ja |
Relevance: | Peer reviewed Publikation in Master Journal List |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |