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Feature-Based Proposal Density Optimization for Nonlinear Model Predictive Path Integral Control

  • This paper presents a novel feature-based sampling strategy for nonlinear Model Predictive Path Integral (MPPI) control. In MPPI control, the optimal control is calculated by solving a stochastic optimal control problem online using the weighted inference of stochastic trajectories. While the algorithm can be excellently parallelized the closed- loop performance is dependent on the information quality of the drawn samples. Because these samples are drawn using a proposal density, its quality is crucial for the solver and thus the controller performance. In classical MPPI control, the explored state-space is strongly constrained by assumptions that refer to the control value variance, which are necessary for transforming the 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 used to determine the proposal density and thus, the 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. Further, the developed algorithm is applied on an autonomous vessel that follows a track and concurrently avoids collisions using an emergency braking feature.

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Metadaten
Author:Hannes HomburgerORCiD, Stefan WirtensohnORCiD, Moritz DiehlORCiD, Johannes ReuterORCiD
Parent Title (English):Proceedings of the 6th IEEE Conference on Control Technology and Applications (CCTA 2022), August 23-25, Trieste, Italy
Publisher:IEEE
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Release Date:2022/11/10
Tag:Predictive control; Nonlinear systems; Ships and offshore vessels
Page Number:6
Institutes:Institut für Systemdynamik - ISD
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (English):License LogoLizenzbedingungen IEEE