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 $L_1$ 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 $\mathbf{1000}$ paired Monte Carlo trials with gravity, SRP, and diffusion mismatch, $L_1$-DRAC adaptive control reduces the keep-out violation rate from $\mathbf{32.7\%}$ to $\mathbf{0.2\%}$ and the physical collision rate from $\mathbf{16.4\%}$ to zero, without actuator saturation.

Systems and Control (eess.SY); Robotics (cs.RO); Functional Analysis (math.FA); Optimization and Control (math.OC); Probability (math.PR)

arXiv:2610.12774 [eess.SY]

(or arXiv:2610.12774v1 [eess.SY] for this version)

https://doi.org/10.48550/arXiv.2610.12774 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://space-robots.org/events/iros2026; https://openreview.net/group?id=IEEE.org/IROS/2026/Workshop/SRW

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