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This content will become publicly available on October 18, 2024

Title: Backup Plan Constrained Model Predictive Control with Guaranteed Stability
Safe control designs for robotic systems remain challenging because of the difficulties of explicitly solving optimal control with nonlinear dynamics perturbed by stochastic noise. However, recent technological advances in computing devices enable online optimization or sampling-based methods to solve control problems. For example, Control Barrier Functions (CBFs) have been proposed to numerically solve convex optimization problems that ensure the control input to stay in the safe set. Model Predictive Path Integral (MPPI) control uses forward sampling of stochastic differential equations to solve optimal control problems online. Both control algorithms are widely used for nonlinear systems because they avoid calculating the derivatives of the nonlinear dynamic functions. In this paper, we use Stochastic Control Barrier Functions (SCBFs) constraints to limit sample regions in the samplingbased algorithm, ensuring safety in a probabilistic sense and improving sample efficiency with a stochastic differential equation. We also show that our algorithm needs fewer samples than the original MPPI algorithm does by providing a sampling complexity analysis.  more » « less
Award ID(s):
1932529
NSF-PAR ID:
10482834
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
Journal of Guidance, Control, and Dynamics
ISSN:
0731-5090
Page Range / eLocation ID:
1 to 14
Subject(s) / Keyword(s):
["Path Integral Methods, Stochastic Control, Barrier Functions"]
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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