arXiv subjects

Daniel C. Qi

Publications and source records attributed to Daniel C. Qi.

3 recordsLinked to original sources

arXiv2026

Generalizing Output-Feedback Covariance Steering to Incorporate Non-Orthogonal Estimation Errors

This paper addresses the problem of steering a state distribution over a finite horizon in discrete time with output feedback. The incorporation of output feedback introduces additional challenges arising from the statistical coupling between the true state distribution and the corresponding filtered state distribution. In particular, this paper extends existing covariance steering formulations to scenarios in which estimation errors are not orthogonal to the state estimates. This paper presents a sequential convex programming framework with rank constraints to solve this more general covariance steering with output feedback problem. The proposed approach is validated through numerical examples and Monte Carlo simulations, including cases with non-orthogonal estimation errors that prior techniques cannot address.

math.OC↗

arXiv2025

Convex Bound of Nonlinear Dynamical Errors for Covariance Steering

Applying linear controllers to nonlinear systems requires the dynamical linearization about a reference. In highly nonlinear environments such as cislunar space, the region of validity for these linearizations varies widely and can negatively affect controller performance if not carefully formulated. This paper presents a formulation that minimizes the nonlinear errors experienced by linear covariance controllers. The formulation involves upper-bounding the remainder term from the linearization process using higher-order terms in a Taylor series expansion, and resolving it into a convex function. This can serve as a cost function for controller gain optimization, and its convex nature allows for efficient solutions through convex optimization. This formulation is then demonstrated and compared with the current methods within a halo orbit stationkeeping scenario. The results show that the formulation proposed in this paper maintains the Gaussianity of the distribution in nonlinear simulations more effectively, thereby allowing the linear covariance controller to perform more as intended in nonlinear environments.

math.OC↗

arXiv2025

Non-Gaussian Distribution Steering in Nonlinear Dynamics with Conjugate Unscented Transformation

In highly nonlinear systems such as the ones commonly found in astrodynamics, Gaussian distributions generally evolve into non-Gaussian distributions. This paper introduces a method for effectively controlling non-Gaussian distributions in nonlinear environments using optimized linear feedback control. This paper utilizes Conjugate Unscented Transformation to quantify the higher-order statistical moments of non-Gaussian distributions. The formulation focuses on controlling and constraining the sigma points associated with the uncertainty quantification, which would thereby reflect the control of the entire distribution and constraints on the moments themselves. This paper develops an algorithm to solve this problem with sequential convex programming, and it is demonstrated through a two-body and three-body example. The examples show that individual moments can be directly controlled, and the moments are accurately approximated for non-Gaussian distributions throughout the controller's time horizon in nonlinear dynamics.

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