Distributionally Robust Adaptive Iterative Covariance Steering for Small-Body Proximity Operations

arXiv:2610.12774v1 Announce Type: cross 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.

Source: arXiv cs.RO | 2026-10-12