Data-Driven Stabilisation of Unstable Periodic Orbits of the Three-Body Problem
Owen M. Brook, Jason J. Bramburger, Davide Amato, Urban Fasel

TL;DR
This paper introduces a data-driven, interpretable method for stabilizing unstable periodic orbits in the three-body problem, leveraging local manifold structures and minimal data for efficient control and stabilization.
Contribution
It presents a novel, sample-efficient approach to discover and stabilize UPOs in the 3BP using local Poincaré maps and convex optimization for control impulses.
Findings
Achieved accurate local Poincaré maps with as few as 55 data points.
Successfully stabilized multiple UPOs using small velocity impulses.
Demonstrated computational efficiency and potential for broad applications.
Abstract
Many different models of the physical world exhibit chaotic dynamics, from fluid flows and chemical reactions to celestial mechanics. The study of the three-body problem (3BP) and its many different families of unstable periodic orbits (UPOs) have provided fundamental insight into chaotic dynamics as far back as the 19th century. The 3BP, a conservative system, is inherently challenging to sample due to its volume-preservation property. In this paper we present an interpretable data-driven approach for the state-dependent control of UPOs in the 3BP, through leveraging the inherent sensitivity of chaos and the local manifold structure. We overcome the sampling challenge by utilising prior knowledge of UPOs and a novel augmentation strategy. This enables sample-efficient discovery of a verifiable and accurate local Poincar\'{e} map in as few as 55 data points. We suggest that the…
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