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

TL;DR

This paper extends covariance steering methods to handle non-orthogonal estimation errors in output feedback scenarios, using a new framework and sequential convex programming, validated through numerical experiments.

Contribution

It introduces a general framework for non-orthogonal estimation errors in output-feedback covariance steering, expanding the applicability of existing methods.

Findings

Successfully incorporates non-orthogonal estimation errors into covariance steering.

Validates the approach with numerical examples and Monte Carlo simulations.

Addresses limitations of prior techniques that assumed orthogonal errors.

Abstract

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 distribution steering formulations to scenarios in which estimation errors are not orthogonal to the state estimates. A general framework is developed to capture this non-orthogonality, and the resulting problem is formulated in a form solvable via sequential convex programming with rank constraints. The proposed approach generalizes existing methods and is validated through numerical examples and Monte Carlo simulations, including cases with non-orthogonal estimation errors that prior techniques cannot address.