Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2404.09869

A Systematic Methodology for Modeling and Attitude Control of Multi-body Space Telescopes

Yaguang Yang·William Bentz·Lia Lewis
View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper derives a symbolic multi-body rigid nonlinear model for a space telescope using Stoneking's implementation of Kane's method. This symbolic nonlinear model is linearized using Matlab symbolic functions {\tt diff} and {\tt inv} because the analytic linearization is intractable for manual derivation. The linearized system model is then used to design the controllers using both linear quadratic regulator (LQR) and robust pole assignment methods. The closed-loop systems for the two designs are simulated using both the rigid model as well as a second model containing flexible modes. The performances of the two designs are compared based on the simulation testing results. Our conclusion is that the robust pole assignment design offers better performance than that of the LQR system in terms of actuator usage and pointing accuracy. However, the LQR approach remains an effective first design step that can inform the selection of real eigenvalues for robust pole assignment. The proposed method may be used for the modeling and controller designs for various multi-body systems.

Explore related subjects

math.OC

Keep this discovery

BibTeXRIS

Yaguang Yang, William Bentz, Lia Lewis. 2024-04-15. A Systematic Methodology for Modeling and Attitude Control of Multi-body Space Telescopes. https://doi.org/10.1109/taes.2024.3390648

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Deterministic and Random Bipartite Matching on General Networks: Convex Flow Reformulation, Asymptotic Properties, and Fast Algorithms

Minimum-distance bipartite matching on general networks has numerous applications various fields. This paper first focuses on deterministic problems and presents an exact edgewise-separable convex-flow reformulation. By introducing a smooth monotone rearrangement approximation of the edge-wise imbalance profiles, the convex-flow reformulation's can be solved efficiently. If we further conduct a first-order resistance-based approximation of the convex program, a one-step Laplacian-based estimator can be analytically derived in closed forms. The paper also studies random problems where supply and demand points are randomly distributed. We show that the expected optimal matching distance scales with the square root of the number of points if the supply/demand point distributions are identical, or linearly otherwise. In the former case, the optimal flow is proven to be centered, symmetric, and sub-Gaussian. In the latter case, the limiting resistance network characterizes how supply-demand imbalance is redistributed and motivates a fast algorithm that approximate the optimal flow based on the limiting resistance. Numerical experiments show that the proposed estimators closely approximate the exact matching cost while substantially reducing computation time. The proven theoretical properties of the random matching solution are numerically verified by large-scale Monte Carlo simulations.

math.OC↗
arXiv2026

Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set

Data-driven distributionally robust optimization (DRO) typically treats the conditional outcome law as fixed and uses ambiguity sets to capture estimation error. This paper studies latent distributional heterogeneity, where each instance has an unobserved law but contributes only one observation, so uncertainty persists even if the mixture law is known. We propose Conformal-DRO, which uses nested conformal regions to construct an ambiguity set for the future latent law. Under exchangeability, the set covers this law with probability at least $1-\alpha$ in finite samples, without estimating underlying latent laws or their mixing mechanism. The conformal path induces a data-driven transport geometry, while $\alpha$ determines the radius. The worst-case problem reduces to a finite linear program over conformal shells and admits sparse adversarial solutions. The resulting robust value provides a finite-sample certificate for the selected decision's expected cost.

math.OC↗
arXiv2026

The best approximation tuple: an extension of the Cheney-Goldstein algorithm and results to the multiple sets case

In this paper we extend the algorithm and several results published in the celebrated 1959 paper of Cheney and Goldstein about the best approximation pair (BAP) problem in two separate directions. One is the consideration of more than two sets. The other is the ability to handle each set as an intersections of a finite family of sets. We call the resulting problem the "Best Approximation Tuple (BAT) problem". The fundamental observation that leads to this generalizations is to recognize and handle one set (the "pivot set") as different from the remaining sets (the "satellite sets") instead of seeking cycles as the minimizers of a target functional. This enable us to overcome a certain theoretical obstacle related to cycles and minimizers of general functionals. We prove the convergence of the algorithm to the unique solution of the problem in the Euclidean case with strictly convex and compact satellite sets. Because of the lack of Fej\'er monotonicity, our convergence analysis is not standard, and is based on almost unknown properties of orthogonal projections regarding equality and inequality in the definition of nonexpansiveness.

math.OC↗