Distributionally Robust Adaptive Iterative Covariance Steering for Small-Body Proximity Operations
Abstract
Autonomous small-body proximity operations require continuous-thrust guidance under uncertain gravity, solar-radiation pressure (SRP), and stochastic disturbances. We combine iterative covariance steering (iCS) with L1 distributionally robust adaptive control (L1-DRAC) to provide risk-aware collision avoidance despite mismatch between the nominal planning model and the true dynamics. Iterative covariance steering plans a nominal feedback policy satisfying distributionally robust conditional value-at-risk keep-out constraints, while L1-DRAC compensates for dynamics uncertainty and bounds the residual distributional mismatch through a Wasserstein radius used by the planner. In 1000 paired Monte Carlo trials with gravity, SRP, and diffusion mismatch, L1-DRAC adaptive control reduces the keep-out violation rate from 32.7% to 0.2% and the physical collision rate from 16.4% to zero, without actuator saturation.
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BibTeX
@misc{khatana2026distributionally,
title = {{Distributionally Robust Adaptive Iterative Covariance Steering for Small-Body Proximity Operations}},
author = {Vivek Khatana and Aditya Gahlawat and Naira Hovakimyan and Petros Voulgaris},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.12774},
eprint = {2610.12774},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.12774},
}
RIS
TY - UNPB
TI - Distributionally Robust Adaptive Iterative Covariance Steering for Small-Body Proximity Operations
AU - Vivek Khatana
AU - Aditya Gahlawat
AU - Naira Hovakimyan
AU - Petros Voulgaris
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.12774
UR - https://arxiv.org/abs/2610.12774
AB - Autonomous small-body proximity operations require continuous-thrust guidance under uncertain gravity, solar-radiation pressure (SRP), and stochastic disturbances. We combine iterative covariance steering (iCS) with L_1 distributionally robust adaptive control (L_1-DRAC) to provide risk-aware collision avoidance despite mismatch between the nominal planning model and the true dynamics. Iterative covariance steering plans a nominal feedback policy satisfying distributionally robust conditional value-at-risk keep-out constraints, while L_1-DRAC compensates for dynamics uncertainty and bounds the residual distributional mismatch through a Wasserstein radius used by the planner. In 1000 paired Monte Carlo trials with gravity, SRP, and diffusion mismatch, L_1-DRAC adaptive control reduces the keep-out violation rate from 32.7% to 0.2% and the physical collision rate from 16.4% to zero, without actuator saturation.
ER -